Ex3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Ex3_mainLoss+Diag.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JwRTIXeJqab_"
      },
      "source": [
        "## Basic Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4Zqx3ZBOKnO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ce1a6dc-1e06-497a-9303-1acadf80f05b"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import networkx as nx\n",
        "\n",
        "from platform import python_version\n",
        "print(\"Python Version\", python_version())\n",
        "print('PyTorch Version', torch.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python Version 3.7.12\n",
            "PyTorch Version 1.10.0+cu111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evkPHCXNqISe",
        "outputId": "4abbd083-5fa4-49b7-a8f9-f2b059687e00"
      },
      "source": [
        "# Device\n",
        "DEVICE = 'cpu'\n",
        "if torch.cuda.is_available():\n",
        "    DEVICE = \"cuda\"\n",
        "print(\"device =\", DEVICE)\n",
        "# Reproducibility\n",
        "SEED = 3407\n",
        "import random\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed_all(SEED)\n",
        "# Parallel processing\n",
        "NUM_CORES = 20\n",
        "torch.set_num_threads(NUM_CORES)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "device = cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjVT4mJ8qLpp",
        "outputId": "44a29b8b-d131-4cb7-c365-b6d43dbe9fa5"
      },
      "source": [
        "if DEVICE==\"cpu\":\n",
        "    !pip install -q torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cpu.html\n",
        "else:\n",
        "    !pip install -q torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cu111.html\n",
        "import torch_geometric as pyg\n",
        "print('PyTorch Geometric Version', pyg.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 291 kB 2.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 639 kB 33.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 328 kB 39.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 138 kB 49.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 325 kB 5.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 407 kB 51.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 45 kB 3.5 MB/s \n",
            "\u001b[?25h  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "PyTorch Geometric Version 2.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojccKMV1qdrh"
      },
      "source": [
        "## Prepare graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "th2rr-XwqW5z"
      },
      "source": [
        "def to_node_idx(x,y):\n",
        "    return (8*x) + y\n",
        "\n",
        "def to_coord(node_idx):\n",
        "    return node_idx//8, node_idx%8\n",
        "\n",
        "edge_index = [[],[]]\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        if i<7:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j))\n",
        "            edge_index[0].append(to_node_idx(i+1,j));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if j>0:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j-1))\n",
        "                edge_index[0].append(to_node_idx(i+1,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if i>0:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j))\n",
        "            edge_index[0].append(to_node_idx(i-1,j));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if j<7:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j+1))\n",
        "                edge_index[0].append(to_node_idx(i-1,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if j<7:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i,j+1))\n",
        "            edge_index[0].append(to_node_idx(i,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if i<7:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j+1))\n",
        "                edge_index[0].append(to_node_idx(i+1,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if j>0:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i,j-1))\n",
        "            edge_index[0].append(to_node_idx(i,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if i>0:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j-1))\n",
        "                edge_index[0].append(to_node_idx(i-1,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "\n",
        "edge_index = torch.tensor(edge_index, dtype=torch.long).to(DEVICE)\n",
        "COINS = set([10, 30, 50])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n41CppvVqmv9",
        "outputId": "9135a6f3-e832-4390-db1f-0fbf3411f0f6"
      },
      "source": [
        "data = pyg.data.Data(edge_index = edge_index).to(DEVICE)\n",
        "NUM_NODES = data.num_nodes\n",
        "NUM_EDGES = data.num_edges\n",
        "print(\"No. of nodes =\", NUM_NODES)\n",
        "print(\"No. of edges =\", NUM_EDGES)\n",
        "print(\"edge_index.shape =\", data.edge_index.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of nodes = 64\n",
            "No. of edges = 840\n",
            "edge_index.shape = torch.Size([2, 840])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "RJVtbHoVqnr0",
        "outputId": "44edce14-e9a1-49f9-c0a2-97d23c8149a0"
      },
      "source": [
        "graph = pyg.utils.to_networkx(data)\n",
        "pos = {}\n",
        "for idx in range(64):\n",
        "    x,y = to_coord(idx)\n",
        "    pos[idx] = np.array([x,y])\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "color = ['y' for i in range(64)]\n",
        "for coin in COINS:\n",
        "    color[coin] = 'r'\n",
        "nx.drawing.nx_pylab.draw_networkx(graph, node_color=color, pos=pos, node_size=750, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YM9i6NEEqno8"
      },
      "source": [
        "def plot_policy(policy):\n",
        "    u = [[0.75 for i in range(8)] for j in range(8)]\n",
        "    v = [[0.75 for i in range(8)] for j in range(8)]\n",
        "    for state in range(64):\n",
        "        x,y = to_coord(state)\n",
        "        action = np.argmax(policy[state])\n",
        "        if action==0:\n",
        "            u[y][x] = 0\n",
        "            v[y][x] = 0.75\n",
        "        elif action==1:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = 0.75\n",
        "        elif action==2:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = 0\n",
        "        elif action==3:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = -0.75\n",
        "        elif action==4:\n",
        "            u[y][x] = 0\n",
        "            v[y][x] = -0.75\n",
        "        elif action==5:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = -0.75\n",
        "        elif action==6:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = 0\n",
        "        else:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = 0.75\n",
        "\n",
        "    graph = pyg.utils.to_networkx(data)\n",
        "    pos = {}\n",
        "    for idx in range(64):\n",
        "        x,y = to_coord(idx)\n",
        "        pos[idx] = np.array([x,y])\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(8, 8))\n",
        "    color = ['y' for i in range(64)]\n",
        "    for coin in COINS:\n",
        "        color[coin] = 'r'\n",
        "    nx.drawing.nx_pylab.draw_networkx(graph, node_color=color, pos=pos, node_size=750, ax=ax, edgelist=[])\n",
        "    \n",
        "    X = np.arange(0,8,1)\n",
        "    Y = np.arange(0,8,1)\n",
        "\n",
        "    ax.quiver(X, Y, u, v, scale_units='xy', scale=1)\n",
        "    \n",
        "    ax.xaxis.set_ticks([])\n",
        "    ax.yaxis.set_ticks([])\n",
        "    ax.set_aspect('equal')\n",
        "    plt.grid()\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49yosqtWNi7B"
      },
      "source": [
        "## 3. Define random walker and Initialize policy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JWCtmMGjBfr"
      },
      "source": [
        "POLICY = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        POLICY[u] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "        if j<7: # up\n",
        "            POLICY[u][0] = 1.0\n",
        "        if j<7 and i<7: # up-right\n",
        "            POLICY[u][1] = 1.0\n",
        "        if i<7: # right\n",
        "            POLICY[u][2] = 1.0\n",
        "        if i<7 and j>0: # down-right\n",
        "            POLICY[u][3] = 1.0\n",
        "        if j>0: # down\n",
        "            POLICY[u][4] = 1.0\n",
        "        if j>0 and i>0: # down-left\n",
        "            POLICY[u][5] = 1.0\n",
        "        if i>0: # left\n",
        "            POLICY[u][6] = 1.0\n",
        "        if j<7 and i>0: # up-left\n",
        "            POLICY[u][7] = 1.0\n",
        "        POLICY[u] /= sum(POLICY[u])\n",
        "\n",
        "Q = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        Q[u] = {}\n",
        "        if j<7: # up\n",
        "            Q[u][0] = torch.rand(1)\n",
        "        if j<7 and i<7: # up-right\n",
        "            Q[u][1] = torch.rand(1)\n",
        "        if i<7: # right\n",
        "            Q[u][2] = torch.rand(1)\n",
        "        if i<7 and j>0: # down-right\n",
        "            Q[u][3] = torch.rand(1)\n",
        "        if j>0: # down\n",
        "            Q[u][4] = torch.rand(1)\n",
        "        if j>0 and i>0: # down-left\n",
        "            Q[u][5] = torch.rand(1)\n",
        "        if i>0: # left\n",
        "            Q[u][6] = torch.rand(1)\n",
        "        if j<7 and i>0: # up-left\n",
        "            Q[u][7] = torch.rand(1)\n",
        "\n",
        "RET = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        RET[u] = [[], [], [], [], [], [], [], []]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3eBev53qnmX"
      },
      "source": [
        "def sample_episode(walk_len, start_node=None):\n",
        "    if start_node==None:\n",
        "        start_node = np.random.randint(0,NUM_NODES)\n",
        "    states = []\n",
        "    actions = []\n",
        "    rewards = []\n",
        "    curr_state = start_node\n",
        "    while len(states) < walk_len:\n",
        "        states.append(curr_state)\n",
        "        if curr_state in COINS:\n",
        "            rewards.append(1.0)\n",
        "        else:\n",
        "            rewards.append(0.0)\n",
        "        actions.append(np.random.choice(a=[0,1,2,3,4,5,6,7], size=None, replace=True, p=POLICY[curr_state]))\n",
        "        if actions[-1]==0:      # up\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0], to_coord(curr_state)[1]+1)\n",
        "        elif actions[-1]==1:      # up-right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1]+1)\n",
        "        elif actions[-1]==2:    # right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1])\n",
        "        elif actions[-1]==3:      # down-right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1]-1)\n",
        "        elif actions[-1]==4:    # down\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0], to_coord(curr_state)[1]-1)\n",
        "        elif actions[-1]==5:      # down-left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1]-1)\n",
        "        elif actions[-1]==6:    # left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1])\n",
        "        else:      # up-left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1]+1)\n",
        "    return states, actions, rewards"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_MlK2uxrJpM"
      },
      "source": [
        "## Build models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6FJwSCOqnj-"
      },
      "source": [
        "# Set Hyperparameters\n",
        "EMBED_DIM = 512\n",
        "EPS = 1e-15\n",
        "INIT_LR1 = 0.1\n",
        "\n",
        "WALK_LEN = 128\n",
        "INIT_LR2 = 1e-3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgWVxsR1qnhh"
      },
      "source": [
        "class Node2Vec(torch.nn.Module):\n",
        "    def __init__(self, embedding_dim=EMBED_DIM, num_nodes=NUM_NODES, sparse=False):\n",
        "        super(Node2Vec, self).__init__()\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.embedding = torch.nn.Embedding(num_nodes, embedding_dim, sparse=sparse)\n",
        "        self.reset_parameters()\n",
        "\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        self.embedding.reset_parameters()\n",
        "\n",
        "\n",
        "    def forward(self, batch=None):\n",
        "        emb = self.embedding.weight\n",
        "        return emb if batch is None else emb[batch]\n",
        "\n",
        "\n",
        "    def loss(self, pos_rw, neg_rw):\n",
        "        start, rest = pos_rw[:, 0], pos_rw[:, 1:].contiguous()\n",
        "        h_start = self.embedding(start).view(pos_rw.size(0), 1, self.embedding_dim)\n",
        "        h_rest = self.embedding(rest.view(-1)).view(pos_rw.size(0), -1, self.embedding_dim)\n",
        "        out = (h_start * h_rest).sum(dim=-1).view(-1)\n",
        "        pos_loss = -torch.log(torch.sigmoid(out) + EPS).mean()\n",
        "\n",
        "        start, rest = neg_rw[:, 0], neg_rw[:, 1:].contiguous()\n",
        "        h_start = self.embedding(start).view(neg_rw.size(0), 1, self.embedding_dim)\n",
        "        h_rest = self.embedding(rest.view(-1)).view(neg_rw.size(0), -1, self.embedding_dim)\n",
        "\n",
        "        out = (h_start * h_rest).sum(dim=-1).view(-1)\n",
        "        neg_loss = -torch.log(1 - torch.sigmoid(out) + EPS).mean()\n",
        "        return pos_loss + neg_loss\n",
        "\n",
        "model1 = Node2Vec().to(DEVICE)\n",
        "optimizer1 = torch.optim.Adam(model1.parameters(), INIT_LR1)\n",
        "platLR1 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer1, mode='min', factor=0.5, patience=1, threshold=0.0001, \n",
        "                                                    threshold_mode='abs', cooldown=0, min_lr=0, eps=1e-10, verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DvylOvFqneX"
      },
      "source": [
        "def get_contexts(walks, size):\n",
        "    batch = torch.tensor([], dtype=torch.int, requires_grad=False).to(DEVICE)\n",
        "    for sequence in walks:\n",
        "        for i in range(0,len(sequence)-size):\n",
        "            batch = torch.cat((batch, sequence[i:i+size].reshape(1,size)), axis=0)\n",
        "    return batch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_ZEVaearYQI"
      },
      "source": [
        "class InferNet(torch.nn.Module):\n",
        "    def __init__ (self, input_len=EMBED_DIM):\n",
        "        super(InferNet, self).__init__()\n",
        "        self.input_size = input_len\n",
        "        self.lin1 = torch.nn.Linear(self.input_size,512)\n",
        "        self.lin4 = torch.nn.Linear(512,1)\n",
        "\n",
        "    def forward(self, X):\n",
        "        X = torch.tanh(self.lin1(X))\n",
        "        X = self.lin4(X)\n",
        "        return X\n",
        "\n",
        "model2 = InferNet().to(DEVICE)\n",
        "optimizer2 = torch.optim.Adam(model2.parameters(), INIT_LR2)\n",
        "platLR2 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer2, mode='min', factor=0.5, patience=1, threshold=0.0001, \n",
        "                                                    threshold_mode='abs', cooldown=0, min_lr=0, eps=1e-10, verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tKwUWa8rYNv"
      },
      "source": [
        "mse = torch.nn.MSELoss(reduction='sum')\n",
        "def getLoss2(imm_rewards, rewards):\n",
        "    main_loss = mse(torch.sum(imm_rewards), torch.sum(rewards)) / WALK_LEN\n",
        "    return main_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7Q1o1QIrYA-"
      },
      "source": [
        "def printRewards(rewards):\n",
        "    matrix = np.array([[float(rewards[to_node_idx(i,j)][0]) for i in range(8)] for j in range(8)])\n",
        "    cm = sns.light_palette(\"blue\", as_cmap=True)\n",
        "    x=pd.DataFrame(matrix).iloc[::-1]\n",
        "    x=x.style.background_gradient(cmap=cm, axis=None)\n",
        "    display(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqK-2Z1Ur6sT"
      },
      "source": [
        "## Train models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Zppsa_AgrYI4",
        "outputId": "dcd1bdb8-80e4-42ef-f4ce-acfb4925668f"
      },
      "source": [
        "NUM_ITER = 50\n",
        "CONT_SIZE = 3\n",
        "NUM_NEG = 1\n",
        "losses1 = []\n",
        "losses2 = []\n",
        "# GAMMA = 0.5\n",
        "\n",
        "model2.eval()\n",
        "all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)\n",
        "printRewards(imm_rewards)\n",
        "\n",
        "for iter in range(1,NUM_ITER+1):\n",
        "    states, actions, rewards = [], [], []\n",
        "    for start in range(NUM_NODES):\n",
        "        s, a, r = sample_episode(WALK_LEN, start)\n",
        "        states.append(s)\n",
        "        actions.append(a)\n",
        "        rewards.append(r)\n",
        "    neg_samples = torch.randint(low=0, high=NUM_NODES, size=(NUM_NODES,(WALK_LEN*NUM_NEG)-1))\n",
        "    neg_samples = neg_samples.reshape(NUM_NODES, (WALK_LEN*NUM_NEG)-1)\n",
        "\n",
        "    states = torch.tensor(states).to(DEVICE)\n",
        "    actions = torch.tensor(actions).to(DEVICE)\n",
        "    rewards = torch.tensor(rewards).to(DEVICE)\n",
        "    neg_samples = torch.cat((states[:,0].reshape(NUM_NODES,1), neg_samples.to(DEVICE)), axis=1)\n",
        "\n",
        "    model1.train()\n",
        "    optimizer1.zero_grad()\n",
        "    pos = get_contexts(states, CONT_SIZE)\n",
        "    neg = get_contexts(neg_samples, CONT_SIZE)\n",
        "    loss1 = model1.loss(pos.to(DEVICE), neg.to(DEVICE))\n",
        "    loss1.backward()\n",
        "    optimizer1.step()\n",
        "    loss1 = float(loss1)\n",
        "    platLR1.step(loss1)\n",
        "    \n",
        "    model2.train()\n",
        "    loss2 = 0.0\n",
        "    for i in range(NUM_NODES):\n",
        "        optimizer2.zero_grad()\n",
        "        ep_states = model1.forward(states[i,:].reshape(WALK_LEN)).reshape(WALK_LEN,EMBED_DIM)\n",
        "        imm_rewards = model2.forward(ep_states).reshape(WALK_LEN,1)\n",
        "        L = getLoss2(imm_rewards, rewards[i].reshape(WALK_LEN,1))\n",
        "        L.backward()\n",
        "        optimizer2.step()\n",
        "        loss2 += float(L)\n",
        "    loss2 /= float(NUM_NODES)\n",
        "    platLR2.step(loss2)\n",
        "\n",
        "    # if len(losses1)>0:\n",
        "    #     loss1 = (GAMMA*loss1) + ((1-GAMMA)*losses1[-1])\n",
        "    # if len(losses2)>0:\n",
        "    #     loss2 = (GAMMA*loss2) + ((1-GAMMA)*losses2[-1])\n",
        "\n",
        "    print(\"iter : \"+str(iter)+\" |  loss1 : \"+str(np.around(loss1,6))+\" |  loss2 : \"+str(np.around(loss2,6)))\n",
        "    losses1.append(loss1)\n",
        "    losses2.append(loss2)\n",
        "\n",
        "    if iter in [10, 25, 50]:\n",
        "        model2.eval()\n",
        "        all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "        imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)\n",
        "        printRewards(imm_rewards)"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "        }</style><table id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.476522</td>\n",
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              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row0_col2\" class=\"data row0 col2\" >-0.078334</td>\n",
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              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.112307</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >-0.077897</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >-0.056860</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >-0.193182</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >-0.217227</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.449050</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >-0.253179</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.222002</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >-0.027036</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.246256</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >-0.162212</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >-0.286815</td>\n",
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              "                        <th id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.506386</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.082366</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.306199</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >-0.037394</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >-0.165799</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.143218</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.138173</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >-0.189759</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >-0.488101</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.006839</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.022729</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.290967</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.300160</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >-0.069967</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.169547</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.211054</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.007664</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >-0.277316</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.244599</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.205027</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.196127</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.139028</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.417372</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.360891</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.194938</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.070811</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >-0.375516</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >-0.089531</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.094787</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.238499</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >-0.115760</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >-0.220435</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.131425</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.726761</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.018675</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >-0.322981</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.216040</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.199119</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.626912</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >-0.070740</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.025977</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.212268</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.275828</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.270882</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >-0.411361</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.278527</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >-0.187398</td>\n",
              "                        <td id=\"T_2f702d62_445d_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.193604</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fd1ebc32910>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 1 |  loss1 : 18.854073 |  loss2 : 13.311949\n",
            "iter : 2 |  loss1 : 13.683125 |  loss2 : 0.938823\n",
            "iter : 3 |  loss1 : 12.18894 |  loss2 : 0.300061\n",
            "iter : 4 |  loss1 : 11.846163 |  loss2 : 0.108441\n",
            "iter : 5 |  loss1 : 11.615489 |  loss2 : 0.062022\n",
            "iter : 6 |  loss1 : 11.182859 |  loss2 : 0.030044\n",
            "iter : 7 |  loss1 : 10.946642 |  loss2 : 0.050167\n",
            "iter : 8 |  loss1 : 10.730477 |  loss2 : 0.01184\n",
            "iter : 9 |  loss1 : 10.536671 |  loss2 : 0.05752\n",
            "iter : 10 |  loss1 : 10.206418 |  loss2 : 0.120273\n"
          ]
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          "output_type": "display_data",
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              "                <tr>\n",
              "                        <th id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.152879</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.028635</td>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.059580</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.236482</td>\n",
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              "                        <th id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.711500</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.185879</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.017316</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.017447</td>\n",
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              "                        <th id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.149485</td>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.042362</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.170940</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.227016</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >-0.125368</td>\n",
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              "                        <th id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.005452</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.031569</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.212119</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.147939</td>\n",
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              "                        <th id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.297949</td>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.004908</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.012848</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >-0.089612</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.169635</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.307138</td>\n",
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              "                        <th id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.362677</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.045279</td>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.721481</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.178396</td>\n",
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              "                        <th id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.068011</td>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.009069</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.070080</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.126494</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.030849</td>\n",
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              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.331818</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.089634</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.012411</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >-0.262707</td>\n",
              "                        <td id=\"T_463b7236_445d_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >-0.246808</td>\n",
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              "    </tbody></table>"
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              "<pandas.io.formats.style.Styler at 0x7fd1ea146090>"
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        {
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          "text": [
            "iter : 11 |  loss1 : 10.105071 |  loss2 : 0.044958\n",
            "iter : 12 |  loss1 : 9.960142 |  loss2 : 0.032002\n",
            "iter : 13 |  loss1 : 10.023799 |  loss2 : 0.017139\n",
            "iter : 14 |  loss1 : 10.015869 |  loss2 : 0.011487\n",
            "iter : 15 |  loss1 : 9.71533 |  loss2 : 0.007203\n",
            "iter : 16 |  loss1 : 9.581655 |  loss2 : 0.006629\n",
            "iter : 17 |  loss1 : 9.396087 |  loss2 : 0.005669\n",
            "iter : 18 |  loss1 : 9.42003 |  loss2 : 0.006346\n",
            "iter : 19 |  loss1 : 9.275849 |  loss2 : 0.007067\n",
            "iter : 20 |  loss1 : 9.214088 |  loss2 : 0.003956\n",
            "iter : 21 |  loss1 : 9.250169 |  loss2 : 0.004733\n",
            "iter : 22 |  loss1 : 9.273163 |  loss2 : 0.002434\n",
            "iter : 23 |  loss1 : 9.130705 |  loss2 : 0.002797\n",
            "iter : 24 |  loss1 : 9.053638 |  loss2 : 0.003402\n",
            "iter : 25 |  loss1 : 9.410683 |  loss2 : 0.003558\n"
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              "                <tr>\n",
              "                        <th id=\"T_6787a2e8_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_6787a2e8_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.006660</td>\n",
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              "                        <th id=\"T_6787a2e8_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
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              "                        <th id=\"T_6787a2e8_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
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              "                        <td id=\"T_6787a2e8_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.023442</td>\n",
              "                        <td id=\"T_6787a2e8_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.064349</td>\n",
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              "                        <th id=\"T_6787a2e8_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
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              "                        <th id=\"T_6787a2e8_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
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          "text": [
            "iter : 26 |  loss1 : 8.82815 |  loss2 : 0.002253\n",
            "iter : 27 |  loss1 : 9.114285 |  loss2 : 0.003009\n",
            "iter : 28 |  loss1 : 9.124572 |  loss2 : 0.002721\n",
            "iter : 29 |  loss1 : 9.214433 |  loss2 : 0.002957\n",
            "iter : 30 |  loss1 : 8.875691 |  loss2 : 0.002627\n",
            "iter : 31 |  loss1 : 8.90794 |  loss2 : 0.001684\n",
            "iter : 32 |  loss1 : 8.877952 |  loss2 : 0.002145\n",
            "iter : 33 |  loss1 : 9.084883 |  loss2 : 0.001816\n",
            "iter : 34 |  loss1 : 8.842264 |  loss2 : 0.003046\n",
            "iter : 35 |  loss1 : 8.85383 |  loss2 : 0.0022\n",
            "iter : 36 |  loss1 : 8.732239 |  loss2 : 0.001681\n",
            "iter : 37 |  loss1 : 8.685324 |  loss2 : 0.002293\n",
            "iter : 38 |  loss1 : 8.829358 |  loss2 : 0.002304\n",
            "iter : 39 |  loss1 : 8.813309 |  loss2 : 0.002178\n",
            "iter : 40 |  loss1 : 8.839278 |  loss2 : 0.002521\n",
            "iter : 41 |  loss1 : 8.984371 |  loss2 : 0.001756\n",
            "iter : 42 |  loss1 : 8.931166 |  loss2 : 0.00218\n",
            "iter : 43 |  loss1 : 8.774843 |  loss2 : 0.002287\n",
            "iter : 44 |  loss1 : 8.914617 |  loss2 : 0.001477\n",
            "iter : 45 |  loss1 : 8.895617 |  loss2 : 0.001493\n",
            "iter : 46 |  loss1 : 9.024598 |  loss2 : 0.002269\n",
            "iter : 47 |  loss1 : 8.867477 |  loss2 : 0.002465\n",
            "iter : 48 |  loss1 : 8.628921 |  loss2 : 0.001452\n",
            "iter : 49 |  loss1 : 8.925558 |  loss2 : 0.001548\n",
            "iter : 50 |  loss1 : 8.641019 |  loss2 : 0.002512\n"
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              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row2_col4,#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col7{\n",
              "            background-color:  #c4c4f5;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col0{\n",
              "            background-color:  #f0f0f3;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col2,#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col4{\n",
              "            background-color:  #d7d7f4;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col3,#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col7{\n",
              "            background-color:  #d9d9f4;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col4,#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col7{\n",
              "            background-color:  #d4d4f4;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col5,#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col2{\n",
              "            background-color:  #cacaf5;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col6{\n",
              "            background-color:  #e1e1f4;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col0{\n",
              "            background-color:  #b5b5f6;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col3,#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col5{\n",
              "            background-color:  #dadaf4;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col5{\n",
              "            background-color:  #c9c9f5;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col0{\n",
              "            background-color:  #d5d5f4;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col1{\n",
              "            background-color:  #0202ff;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col2,#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col4{\n",
              "            background-color:  #cecef4;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col6{\n",
              "            background-color:  #0000ff;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col0{\n",
              "            background-color:  #ccccf5;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col1{\n",
              "            background-color:  #c5c5f5;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col3,#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col7{\n",
              "            background-color:  #e0e0f4;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col4{\n",
              "            background-color:  #cecef5;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col6,#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col7{\n",
              "            background-color:  #c3c3f5;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col1{\n",
              "            background-color:  #e7e7f3;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col2{\n",
              "            background-color:  #bcbcf5;\n",
              "            color:  #000000;\n",
              "        }#T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col5{\n",
              "            background-color:  #e2e2f3;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >-0.003426</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.037842</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.093030</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.024819</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.101947</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >-0.020161</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >-0.002553</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.004272</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >-0.085772</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >-0.004128</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >-0.050178</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.835663</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.070919</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >-0.100992</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.077358</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >-0.029246</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >-0.008172</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.075791</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.047265</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.018348</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.054789</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >-0.029594</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.039064</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >-0.027306</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >-0.131558</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.004384</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >-0.022189</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >-0.032692</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.010930</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.029209</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.065601</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.030819</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.115530</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.068269</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.028760</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >-0.035304</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >-0.022153</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.033556</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.069574</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.052204</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.016105</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.858829</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.012662</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.024987</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.012918</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >-0.036983</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.869071</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >-0.010813</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.022587</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.049758</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.037231</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >-0.063799</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.016747</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.077065</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.059448</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.059577</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >-0.026140</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >-0.092128</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.089829</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.050354</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.047463</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >-0.069771</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >-0.026747</td>\n",
              "                        <td id=\"T_9f4e4a92_445d_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >-0.064133</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fd1fd6e37d0>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "NpSZTqB_rYGL",
        "outputId": "af2dd3fb-3d0c-4fae-e6a8-0fdacb3f46b7"
      },
      "source": [
        "printRewards(rewards[:,0].reshape(NUM_NODES,1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fd1fd783fd0>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "LjyGbLSKsa1K",
        "outputId": "f6fc923f-35f0-4b63-ad32-d6950e3ff723"
      },
      "source": [
        "x = [i for i in range(1,NUM_ITER+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, losses1, 'r', label='Node2Vec Loss')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"Node2Vec Loss vs Iterations\")\n",
        "fig.savefig('Node2VecLoss_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "V1gwNscPs3jJ",
        "outputId": "140e7ef4-afe9-4913-fad2-bc64a39d5335"
      },
      "source": [
        "x = [i for i in range(1,NUM_ITER+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, list(losses2), 'g', label='InferNet Loss')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"InferNet Loss vs Iterations\")\n",
        "fig.savefig('InferNetLoss_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3UheoJv6s7E0"
      },
      "source": [
        "## Improve policy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyLo7KEIrYDo"
      },
      "source": [
        "def improvePolicy(policy, Q, state, eps=0.1):\n",
        "    q = torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "    for a in [0, 1, 2, 3, 4, 5, 6, 7]:\n",
        "        if a in Q[state].keys():\n",
        "            q[a] = Q[state][a]\n",
        "\n",
        "    policy[state] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "    idx = 0\n",
        "    _, indices = torch.sort(q, descending=True)\n",
        "    policy[state][indices[idx]] = (1.0-eps)\n",
        "    # if indices[0]==7:\n",
        "        # print(\"state =\", state)\n",
        "        # print(\"policy =\", policy[state])\n",
        "    while ((state+1)%8 == 0 and indices[idx]==0) or (((state+1)%8 == 0 or (state>=56 and state<=63)) and indices[idx]==1) or (state>=56 and state<=63 and indices[idx]==2) or (((state>=56 and state<=63) or state%8 == 0) and indices[idx]==3) or (state%8 == 0 and indices[idx]==4) or ((state%8 == 0 or (state>=0 and state<=7)) and indices[idx]==5) or (state>=0 and state<=7 and indices[idx]==6) or (((state+1)%8 == 0 or (state>=0 and state<=7)) and indices[idx]==7):\n",
        "        policy[state][indices[idx]] = 0.0\n",
        "        idx += 1\n",
        "        policy[state][indices[idx]] = (1.0-eps)\n",
        "\n",
        "    num_actions = 8.0\n",
        "    if (state+1)%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if (state+1)%8 == 0 or (state>=56 and state<=63):\n",
        "        num_actions -= 1.0\n",
        "    if state>=56 and state<=63:\n",
        "        num_actions -= 1.0\n",
        "    if (state>=56 and state<=63) or state%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if state%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if state%8 == 0 or (state>=0 and state<=7):\n",
        "        num_actions -= 1.0\n",
        "    if state>=0 and state<=7:\n",
        "        num_actions -= 1.0\n",
        "    if (state+1)%8 == 0 or (state>=0 and state<=7):\n",
        "        num_actions -= 1.0\n",
        "\n",
        "    if not ((state+1)%8 == 0):\n",
        "        policy[state][0] += (eps/num_actions)\n",
        "    if not ((state+1)%8 == 0 or (state>=56 and state<=63)):\n",
        "        policy[state][1] += (eps/num_actions)\n",
        "    if not (state>=56 and state<=63):\n",
        "        policy[state][2] += (eps/num_actions)\n",
        "    if not ((state>=56 and state<=63) or state%8 == 0):\n",
        "        policy[state][3] += (eps/num_actions)\n",
        "    if state%8 != 0:\n",
        "        policy[state][4] += (eps/num_actions)\n",
        "    if not (state%8 == 0 or (state>=0 and state<=7)):\n",
        "        policy[state][5] += (eps/num_actions)\n",
        "    if not (state>=0 and state<=7):\n",
        "        policy[state][6] += (eps/num_actions)\n",
        "    if not ((state+1)%8 == 0 or (state>=0 and state<=7)):\n",
        "        policy[state][7] += (eps/num_actions)    \n",
        "    return policy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6P5N73szrYLY"
      },
      "source": [
        "def walk_freq():\n",
        "    freq = {}\n",
        "    for i in range(1000):\n",
        "        for start in range(NUM_NODES):\n",
        "            states, a, r = sample_episode(8, start)\n",
        "            for s in states:\n",
        "                if s not in freq.keys():\n",
        "                    freq[s] = 0\n",
        "                freq[s] += 1\n",
        "    matrix = np.array([[int(freq[to_node_idx(i,j)]) for i in range(8)] for j in range(8)])\n",
        "    cm = sns.light_palette(\"green\", as_cmap=True)\n",
        "    x=pd.DataFrame(matrix).iloc[::-1]\n",
        "    x=x.style.background_gradient(cmap=cm, axis=None)\n",
        "    display(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ImEZzhAGs3eM",
        "outputId": "478c5fec-8d91-4a02-8b5f-46e46843cddf"
      },
      "source": [
        "plot_policy(POLICY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRSMeU2ms2x_"
      },
      "source": [
        "model1.eval()\n",
        "model2.eval()\n",
        "all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Asl5Pz8WrX-S",
        "outputId": "d7e56b40-49a2-43c2-ebac-46e4172224d3"
      },
      "source": [
        "cum_reward = []\n",
        "epsilon = 0.98\n",
        "model2.eval()\n",
        "NUM_ITER = 300\n",
        "WALK_LEN = 5\n",
        "walk_freq()\n",
        "\n",
        "for iter in range(1,NUM_ITER+1):\n",
        "    states, actions, rewards = [], [], []\n",
        "    for start in range(NUM_NODES):\n",
        "        s, a, r = sample_episode(WALK_LEN, start)\n",
        "        states.append(s)\n",
        "        actions.append(a)\n",
        "        rewards.append(r)\n",
        "    \n",
        "    for i in range(NUM_NODES):\n",
        "        G = 0.0\n",
        "        for j in reversed(range(WALK_LEN-1)):\n",
        "            G += float(imm_rewards[states[i][j+1]])\n",
        "            s = states[i][j]\n",
        "            a = actions[i][j]\n",
        "            vis_prev = False\n",
        "            for k in range(j):\n",
        "                if states[i][k]==s and actions[i][k]==a:\n",
        "                    vis_prev = True\n",
        "                    break\n",
        "            if vis_prev:\n",
        "                continue\n",
        "            \n",
        "            RET[s][a].append(G)\n",
        "            Q[s][a] = np.mean(RET[s][a])\n",
        "            POLICY = improvePolicy(POLICY, Q, s, epsilon)\n",
        "\n",
        "    epsilon *= 0.98\n",
        "    rewards = torch.tensor(rewards).to(DEVICE)\n",
        "    \n",
        "    cm = float(torch.sum(rewards))/float(NUM_NODES)\n",
        "    # if len(cum_reward)>0:\n",
        "        # cm = (GAMMA*cm) + ((1-GAMMA)*cum_reward[-1])\n",
        "    cum_reward.append(cm)\n",
        "\n",
        "    print(\"iter : \"+str(iter)+\" |  cum_reward : \"+str(cum_reward[-1]))\n",
        "    if iter in [5, 10, 20, 30, 50, 75, 150, 300]:\n",
        "        walk_freq()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                <tr>\n",
              "                        <th id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >4415</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >6926</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row0_col2\" class=\"data row0 col2\" >6688</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row0_col3\" class=\"data row0 col3\" >6549</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >6401</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >6392</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >6791</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >4530</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >6846</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >10284</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >9668</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >9459</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >9404</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >9643</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >10062</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >6822</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >6586</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >9626</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >9235</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >8995</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8994</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >9022</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >9501</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >6532</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >6294</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >9238</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >8706</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >8629</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >8797</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >9029</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >9413</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >6382</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6409</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >9061</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >8788</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >8525</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >8709</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8870</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >9465</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >6294</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >6654</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >9628</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >9100</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >8882</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >8969</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >9181</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >9571</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >6651</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >7068</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >10317</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >9632</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >9447</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >9431</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >9623</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >10122</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >6951</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >4640</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >7036</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >6509</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >6393</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >6452</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >6471</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >6798</td>\n",
              "                        <td id=\"T_ae0107a0_445d_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >4494</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
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            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fd1e9c9d290>"
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        {
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          "name": "stdout",
          "text": [
            "iter : 1 |  cum_reward : 0.265625\n",
            "iter : 2 |  cum_reward : 0.21875\n",
            "iter : 3 |  cum_reward : 0.328125\n",
            "iter : 4 |  cum_reward : 0.3125\n",
            "iter : 5 |  cum_reward : 0.40625\n"
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              "            background-color:  #c0dec0;\n",
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              "            background-color:  #cbe4cb;\n",
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              "                <tr>\n",
              "                        <th id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >4025</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >6638</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row0_col2\" class=\"data row0 col2\" >7761</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row0_col3\" class=\"data row0 col3\" >6948</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >8254</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >6203</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >6104</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >4073</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >5858</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >9222</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >9189</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >14796</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >9923</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >8927</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >10694</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >6776</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >5463</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >8555</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >9122</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >9170</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8941</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >8565</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >9148</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >5695</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >5647</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >8477</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >8209</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >7858</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >7530</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >7655</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >8099</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >5583</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6484</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >9810</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >11474</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >8517</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >7499</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8803</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >8807</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >6475</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >6313</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >13738</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >9727</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >8854</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >8215</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >9642</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >15195</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >6590</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >6387</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >9645</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >8613</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >8676</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >9592</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >11512</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >9853</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >8401</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >4032</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >5898</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >6067</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >5544</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >5720</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >6036</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >6568</td>\n",
              "                        <td id=\"T_bc004c08_445d_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >4205</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
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              "<pandas.io.formats.style.Styler at 0x7fd1e9e55110>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 6 |  cum_reward : 0.359375\n",
            "iter : 7 |  cum_reward : 0.359375\n",
            "iter : 8 |  cum_reward : 0.46875\n",
            "iter : 9 |  cum_reward : 0.546875\n",
            "iter : 10 |  cum_reward : 0.453125\n"
          ]
        },
        {
          "output_type": "display_data",
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              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col6{\n",
              "            background-color:  #cce4cc;\n",
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              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col7{\n",
              "            background-color:  #d1e6d1;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col0{\n",
              "            background-color:  #d7e9d7;\n",
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              "            background-color:  #b1d7b1;\n",
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              "            background-color:  #8fc68f;\n",
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              "            background-color:  #058205;\n",
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              "            background-color:  #90c790;\n",
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              "            background-color:  #acd4ac;\n",
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              "            background-color:  #87c287;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col7,#T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col6{\n",
              "            background-color:  #cfe5cf;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col0{\n",
              "            background-color:  #dfeddf;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col1,#T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col5,#T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col4{\n",
              "            background-color:  #b9dbb9;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col2{\n",
              "            background-color:  #b2d7b1;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col3{\n",
              "            background-color:  #92c892;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col4{\n",
              "            background-color:  #9fce9f;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col6{\n",
              "            background-color:  #bbdcbb;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col7,#T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col3{\n",
              "            background-color:  #d8ead8;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col0{\n",
              "            background-color:  #dbebdb;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col1{\n",
              "            background-color:  #bcdcbc;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col2,#T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col3{\n",
              "            background-color:  #bdddbd;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col3{\n",
              "            background-color:  #bfdebf;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col5,#T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col1{\n",
              "            background-color:  #a8d2a8;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col6{\n",
              "            background-color:  #b3d8b3;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col7{\n",
              "            background-color:  #d4e8d4;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col2{\n",
              "            background-color:  #8ec58e;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col3{\n",
              "            background-color:  #c3e0c3;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col4{\n",
              "            background-color:  #b0d6b0;\n",
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              "            background-color:  #98ca98;\n",
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              "            background-color:  #b8dab8;\n",
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              "            background-color:  #9ccd9c;\n",
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              "            background-color:  #178b17;\n",
              "            color:  #000000;\n",
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              "            background-color:  #afd6af;\n",
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              "            background-color:  #83c083;\n",
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              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col6{\n",
              "            background-color:  #008000;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col0{\n",
              "            background-color:  #c2dfc2;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col1{\n",
              "            background-color:  #99cb99;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col2{\n",
              "            background-color:  #a1cfa1;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col3{\n",
              "            background-color:  #b4d8b4;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col5{\n",
              "            background-color:  #78bb78;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col6{\n",
              "            background-color:  #9dcd9d;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col0{\n",
              "            background-color:  #eaf2ea;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col1,#T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col5{\n",
              "            background-color:  #d3e7d3;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col2{\n",
              "            background-color:  #cde5cd;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col4{\n",
              "            background-color:  #d6e9d6;\n",
              "            color:  #000000;\n",
              "        }#T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col7{\n",
              "            background-color:  #e9f2e9;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >3412</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >6930</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col2\" class=\"data row0 col2\" >8892</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col3\" class=\"data row0 col3\" >7442</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >10877</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >5882</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >5705</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >5405</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >4898</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >7826</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >10322</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >20791</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >10263</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >8141</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >10951</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >5555</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >4380</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >7213</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >7752</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >10078</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >9135</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >7203</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >7029</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >4828</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >4643</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >6998</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >6811</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >6693</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >6900</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >8420</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >7568</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >5212</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6895</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >8459</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >10441</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >6420</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >7883</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >9654</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >13395</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >7266</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >9355</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >19340</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >7950</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >6806</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >7200</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >11300</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >21140</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >6925</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >6518</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >9638</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >8987</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >7544</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >7946</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >12076</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >9251</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >6929</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >3528</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >5238</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >5631</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >4797</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >4969</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >5226</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >5534</td>\n",
              "                        <td id=\"T_c8d73914_445d_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >3604</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fd1fd6c01d0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 11 |  cum_reward : 0.515625\n",
            "iter : 12 |  cum_reward : 0.59375\n",
            "iter : 13 |  cum_reward : 0.78125\n",
            "iter : 14 |  cum_reward : 0.6875\n",
            "iter : 15 |  cum_reward : 0.75\n",
            "iter : 16 |  cum_reward : 0.703125\n",
            "iter : 17 |  cum_reward : 0.59375\n",
            "iter : 18 |  cum_reward : 0.625\n",
            "iter : 19 |  cum_reward : 0.71875\n",
            "iter : 20 |  cum_reward : 0.625\n"
          ]
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              "            background-color:  #cbe4cb;\n",
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              "                <tr>\n",
              "                        <th id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >2653</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >5245</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row0_col2\" class=\"data row0 col2\" >7628</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row0_col3\" class=\"data row0 col3\" >7444</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >16919</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >5267</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >4481</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >2565</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >3543</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >7745</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >9839</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >31671</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >10370</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >7024</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >7753</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >3773</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >3318</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >5252</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >10861</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >10375</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8632</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >5470</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >7639</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >3890</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >3830</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >5513</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >5257</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >5254</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >5387</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >8331</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >6903</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >6067</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >8936</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >8199</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >7575</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >4442</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >5001</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >11344</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >18828</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >8304</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >14867</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >29743</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8197</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >4580</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >5046</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >8177</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >31014</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >6823</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >8301</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >10889</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >8999</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >4691</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >4986</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >13008</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >7890</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >6470</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >3333</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >4629</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >3809</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >3265</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >3586</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >3922</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >4394</td>\n",
              "                        <td id=\"T_d6b55c6e_445d_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >2853</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fd1fd6e3290>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 21 |  cum_reward : 0.796875\n",
            "iter : 22 |  cum_reward : 0.984375\n",
            "iter : 23 |  cum_reward : 0.78125\n",
            "iter : 24 |  cum_reward : 0.890625\n",
            "iter : 25 |  cum_reward : 0.796875\n",
            "iter : 26 |  cum_reward : 0.859375\n",
            "iter : 27 |  cum_reward : 0.953125\n",
            "iter : 28 |  cum_reward : 0.984375\n",
            "iter : 29 |  cum_reward : 1.0\n",
            "iter : 30 |  cum_reward : 0.890625\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "            background-color:  #b3d8b3;\n",
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              "            background-color:  #c7e1c7;\n",
              "            color:  #000000;\n",
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              "            background-color:  #e2efe2;\n",
              "            color:  #000000;\n",
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              "            background-color:  #c0dec0;\n",
              "            color:  #000000;\n",
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              "            background-color:  #d4e8d4;\n",
              "            color:  #000000;\n",
              "        }#T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col2{\n",
              "            background-color:  #e5f0e5;\n",
              "            color:  #000000;\n",
              "        }#T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col3,#T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col4,#T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col7{\n",
              "            background-color:  #e9f2e9;\n",
              "            color:  #000000;\n",
              "        }#T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col5{\n",
              "            background-color:  #e7f1e7;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >2238</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >5567</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row0_col2\" class=\"data row0 col2\" >7887</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row0_col3\" class=\"data row0 col3\" >7656</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >24985</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >9028</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >4129</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >2104</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >2903</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >5570</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >8927</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >42662</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >12745</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >6577</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >5304</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >2598</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >2399</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >3846</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >9859</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >10043</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8005</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >4179</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >3779</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >2624</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >3008</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >4342</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >4015</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >4056</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >4169</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >6452</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >5550</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >5171</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >9117</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >7559</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >6602</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >3311</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >3724</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >12292</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >24495</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >8168</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >20214</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >37254</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >7238</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >3494</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >3900</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >7176</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >38881</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >6379</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >6559</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >11617</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >8317</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >3540</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >3544</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >9447</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >7655</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >6041</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >2701</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >3573</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >3096</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >2540</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >2510</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >2852</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >3402</td>\n",
              "                        <td id=\"T_e4f18bea_445d_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >2425</td>\n",
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              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fd1fd7a7250>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 31 |  cum_reward : 1.015625\n",
            "iter : 32 |  cum_reward : 0.921875\n",
            "iter : 33 |  cum_reward : 1.15625\n",
            "iter : 34 |  cum_reward : 1.125\n",
            "iter : 35 |  cum_reward : 1.015625\n",
            "iter : 36 |  cum_reward : 0.90625\n",
            "iter : 37 |  cum_reward : 0.984375\n",
            "iter : 38 |  cum_reward : 1.234375\n",
            "iter : 39 |  cum_reward : 1.140625\n",
            "iter : 40 |  cum_reward : 1.125\n",
            "iter : 41 |  cum_reward : 1.125\n",
            "iter : 42 |  cum_reward : 1.109375\n",
            "iter : 43 |  cum_reward : 1.28125\n",
            "iter : 44 |  cum_reward : 1.40625\n",
            "iter : 45 |  cum_reward : 1.25\n",
            "iter : 46 |  cum_reward : 1.234375\n",
            "iter : 47 |  cum_reward : 1.1875\n",
            "iter : 48 |  cum_reward : 1.265625\n",
            "iter : 49 |  cum_reward : 1.1875\n",
            "iter : 50 |  cum_reward : 1.3125\n"
          ]
        },
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          "output_type": "display_data",
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              "            background-color:  #cfe5cf;\n",
              "            color:  #000000;\n",
              "        }#T_f299a19c_445d_11ec_87d5_0242ac1c0002row6_col5{\n",
              "            background-color:  #c8e2c8;\n",
              "            color:  #000000;\n",
              "        }#T_f299a19c_445d_11ec_87d5_0242ac1c0002row6_col6{\n",
              "            background-color:  #d7e9d7;\n",
              "            color:  #000000;\n",
              "        }#T_f299a19c_445d_11ec_87d5_0242ac1c0002row7_col2,#T_f299a19c_445d_11ec_87d5_0242ac1c0002row7_col5{\n",
              "            background-color:  #e9f2e9;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1574</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >4379</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row0_col2\" class=\"data row0 col2\" >6868</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row0_col3\" class=\"data row0 col3\" >6557</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >37491</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >8613</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >2308</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1500</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1825</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >2887</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >6524</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >56349</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >12106</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >5321</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >4663</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >1812</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1766</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >2481</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >8180</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >6563</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8476</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >2798</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >2502</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1894</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >3264</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >3883</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >3473</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >3725</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >2771</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >5140</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >4022</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >4246</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >7850</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >5710</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >30936</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >3146</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >2481</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >11725</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >32610</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >7479</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >4124</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >45802</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >7201</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >3300</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >2670</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >5668</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >50901</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >5298</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >3785</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >8017</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >6478</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >2284</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >2359</td>\n",
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              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >6015</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >5069</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >1722</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >2215</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >1930</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1728</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1831</td>\n",
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              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >2291</td>\n",
              "                        <td id=\"T_f299a19c_445d_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1697</td>\n",
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          "text": [
            "iter : 51 |  cum_reward : 1.40625\n",
            "iter : 52 |  cum_reward : 1.34375\n",
            "iter : 53 |  cum_reward : 1.5\n",
            "iter : 54 |  cum_reward : 1.296875\n",
            "iter : 55 |  cum_reward : 1.34375\n",
            "iter : 56 |  cum_reward : 1.421875\n",
            "iter : 57 |  cum_reward : 1.296875\n",
            "iter : 58 |  cum_reward : 1.453125\n",
            "iter : 59 |  cum_reward : 1.390625\n",
            "iter : 60 |  cum_reward : 1.484375\n",
            "iter : 61 |  cum_reward : 1.5\n",
            "iter : 62 |  cum_reward : 1.390625\n",
            "iter : 63 |  cum_reward : 1.546875\n",
            "iter : 64 |  cum_reward : 1.421875\n",
            "iter : 65 |  cum_reward : 1.46875\n",
            "iter : 66 |  cum_reward : 1.578125\n",
            "iter : 67 |  cum_reward : 1.515625\n",
            "iter : 68 |  cum_reward : 1.515625\n",
            "iter : 69 |  cum_reward : 1.46875\n",
            "iter : 70 |  cum_reward : 1.375\n",
            "iter : 71 |  cum_reward : 1.375\n",
            "iter : 72 |  cum_reward : 1.5625\n",
            "iter : 73 |  cum_reward : 1.59375\n",
            "iter : 74 |  cum_reward : 1.421875\n",
            "iter : 75 |  cum_reward : 1.46875\n"
          ]
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              "            background-color:  #ddecdd;\n",
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              "        }</style><table id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1260</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >3623</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row0_col2\" class=\"data row0 col2\" >5845</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row0_col3\" class=\"data row0 col3\" >4939</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >49237</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >7592</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >1678</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1241</td>\n",
              "            </tr>\n",
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              "                        <th id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1416</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >1959</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >4664</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >66404</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >10836</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >3837</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >3858</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >1394</td>\n",
              "            </tr>\n",
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              "                        <th id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1290</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >1706</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >6423</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >4775</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >6500</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >1903</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >1677</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1441</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2518</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >2706</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >2551</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >2589</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1888</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >3924</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >2911</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >3489</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6549</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >3953</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >40611</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >2455</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1726</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >9888</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >43139</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >6264</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >2921</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >53536</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >5531</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >2436</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >1814</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >4046</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >59675</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >3952</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >2727</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >6502</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >5125</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >1615</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >1653</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >7822</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >4400</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >3858</td>\n",
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              "                        <th id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
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              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >1561</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >1517</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1375</td>\n",
              "                        <td id=\"T_00faa47a_445e_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1412</td>\n",
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          "text": [
            "iter : 76 |  cum_reward : 1.625\n",
            "iter : 77 |  cum_reward : 1.59375\n",
            "iter : 78 |  cum_reward : 1.609375\n",
            "iter : 79 |  cum_reward : 1.484375\n",
            "iter : 80 |  cum_reward : 1.4375\n",
            "iter : 81 |  cum_reward : 1.5\n",
            "iter : 82 |  cum_reward : 1.640625\n",
            "iter : 83 |  cum_reward : 1.625\n",
            "iter : 84 |  cum_reward : 1.625\n",
            "iter : 85 |  cum_reward : 1.6875\n",
            "iter : 86 |  cum_reward : 1.578125\n",
            "iter : 87 |  cum_reward : 1.546875\n",
            "iter : 88 |  cum_reward : 1.578125\n",
            "iter : 89 |  cum_reward : 1.609375\n",
            "iter : 90 |  cum_reward : 1.671875\n",
            "iter : 91 |  cum_reward : 1.65625\n",
            "iter : 92 |  cum_reward : 1.65625\n",
            "iter : 93 |  cum_reward : 1.609375\n",
            "iter : 94 |  cum_reward : 1.71875\n",
            "iter : 95 |  cum_reward : 1.65625\n",
            "iter : 96 |  cum_reward : 1.578125\n",
            "iter : 97 |  cum_reward : 1.609375\n",
            "iter : 98 |  cum_reward : 1.703125\n",
            "iter : 99 |  cum_reward : 1.609375\n",
            "iter : 100 |  cum_reward : 1.625\n",
            "iter : 101 |  cum_reward : 1.65625\n",
            "iter : 102 |  cum_reward : 1.5625\n",
            "iter : 103 |  cum_reward : 1.734375\n",
            "iter : 104 |  cum_reward : 1.765625\n",
            "iter : 105 |  cum_reward : 1.6875\n",
            "iter : 106 |  cum_reward : 1.71875\n",
            "iter : 107 |  cum_reward : 1.640625\n",
            "iter : 108 |  cum_reward : 1.765625\n",
            "iter : 109 |  cum_reward : 1.75\n",
            "iter : 110 |  cum_reward : 1.78125\n",
            "iter : 111 |  cum_reward : 1.75\n",
            "iter : 112 |  cum_reward : 1.8125\n",
            "iter : 113 |  cum_reward : 1.8125\n",
            "iter : 114 |  cum_reward : 1.765625\n",
            "iter : 115 |  cum_reward : 1.703125\n",
            "iter : 116 |  cum_reward : 1.6875\n",
            "iter : 117 |  cum_reward : 1.765625\n",
            "iter : 118 |  cum_reward : 1.8125\n",
            "iter : 119 |  cum_reward : 1.828125\n",
            "iter : 120 |  cum_reward : 1.734375\n",
            "iter : 121 |  cum_reward : 1.765625\n",
            "iter : 122 |  cum_reward : 1.71875\n",
            "iter : 123 |  cum_reward : 1.796875\n",
            "iter : 124 |  cum_reward : 1.765625\n",
            "iter : 125 |  cum_reward : 1.71875\n",
            "iter : 126 |  cum_reward : 1.734375\n",
            "iter : 127 |  cum_reward : 1.765625\n",
            "iter : 128 |  cum_reward : 1.75\n",
            "iter : 129 |  cum_reward : 1.796875\n",
            "iter : 130 |  cum_reward : 1.671875\n",
            "iter : 131 |  cum_reward : 1.75\n",
            "iter : 132 |  cum_reward : 1.84375\n",
            "iter : 133 |  cum_reward : 1.828125\n",
            "iter : 134 |  cum_reward : 1.8125\n",
            "iter : 135 |  cum_reward : 1.765625\n",
            "iter : 136 |  cum_reward : 1.78125\n",
            "iter : 137 |  cum_reward : 1.78125\n",
            "iter : 138 |  cum_reward : 1.828125\n",
            "iter : 139 |  cum_reward : 1.828125\n",
            "iter : 140 |  cum_reward : 1.8125\n",
            "iter : 141 |  cum_reward : 1.8125\n",
            "iter : 142 |  cum_reward : 1.828125\n",
            "iter : 143 |  cum_reward : 1.859375\n",
            "iter : 144 |  cum_reward : 1.796875\n",
            "iter : 145 |  cum_reward : 1.84375\n",
            "iter : 146 |  cum_reward : 1.828125\n",
            "iter : 147 |  cum_reward : 1.78125\n",
            "iter : 148 |  cum_reward : 1.84375\n",
            "iter : 149 |  cum_reward : 1.828125\n",
            "iter : 150 |  cum_reward : 1.78125\n"
          ]
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              "                <tr>\n",
              "                        <th id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1047</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >3101</td>\n",
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              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >64679</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >5613</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >1109</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1028</td>\n",
              "            </tr>\n",
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              "                        <th id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1060</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >1150</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >2545</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >76648</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >7200</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >1728</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >3118</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >1053</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1049</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >1097</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >3819</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >2536</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >3797</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >1139</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >1098</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1075</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2091</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >1397</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >1341</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >1352</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1098</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >2472</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1395</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >2341</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >4691</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >1693</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >54496</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1331</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1089</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >6301</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >58344</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >3895</td>\n",
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              "                        <th id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
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              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >63166</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >2915</td>\n",
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              "                        <th id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
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              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >4466</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >3502</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >1082</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >1094</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >5572</td>\n",
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              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >1084</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >1058</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1056</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1071</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >1069</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >1091</td>\n",
              "                        <td id=\"T_108c8ba6_445e_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1055</td>\n",
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              "    </tbody></table>"
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              "<pandas.io.formats.style.Styler at 0x7fd1e9c8e050>"
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          "text": [
            "iter : 151 |  cum_reward : 1.8125\n",
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              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row0_col4\" class=\"data row0 col4\" >68802</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row0_col5\" class=\"data row0 col5\" >5031</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row0_col6\" class=\"data row0 col6\" >1005</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1002</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1005</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >1005</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row1_col2\" class=\"data row1 col2\" >2029</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row1_col3\" class=\"data row1 col3\" >78923</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row1_col4\" class=\"data row1 col4\" >6079</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row1_col5\" class=\"data row1 col5\" >1034</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row1_col6\" class=\"data row1 col6\" >3001</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row1_col7\" class=\"data row1 col7\" >1001</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1004</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >1004</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row2_col2\" class=\"data row2 col2\" >3035</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row2_col3\" class=\"data row2 col3\" >2042</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row2_col4\" class=\"data row2 col4\" >3045</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row2_col5\" class=\"data row2 col5\" >1005</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row2_col6\" class=\"data row2 col6\" >1004</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1003</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2004</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >1015</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row3_col2\" class=\"data row3 col2\" >1017</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row3_col3\" class=\"data row3 col3\" >1027</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1008</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row3_col5\" class=\"data row3 col5\" >2025</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1018</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row3_col7\" class=\"data row3 col7\" >2020</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >4032</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >1038</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row4_col2\" class=\"data row4 col2\" >58747</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1016</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1003</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row4_col5\" class=\"data row4 col5\" >5054</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row4_col6\" class=\"data row4 col6\" >62767</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row4_col7\" class=\"data row4 col7\" >3041</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >1024</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >65838</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row5_col2\" class=\"data row5 col2\" >2047</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row5_col3\" class=\"data row5 col3\" >1012</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row5_col4\" class=\"data row5 col4\" >1006</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row5_col5\" class=\"data row5 col5\" >1044</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row5_col6\" class=\"data row5 col6\" >71868</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row5_col7\" class=\"data row5 col7\" >1025</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >1021</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >4024</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row6_col2\" class=\"data row6 col2\" >3015</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row6_col3\" class=\"data row6 col3\" >1004</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row6_col4\" class=\"data row6 col4\" >1003</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row6_col5\" class=\"data row6 col5\" >5034</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row6_col6\" class=\"data row6 col6\" >2020</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row6_col7\" class=\"data row6 col7\" >2013</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >1000</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >1008</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row7_col2\" class=\"data row7 col2\" >1003</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1000</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1005</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row7_col5\" class=\"data row7 col5\" >1002</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row7_col6\" class=\"data row7 col6\" >1004</td>\n",
              "                        <td id=\"T_235ba6a4_445e_11ec_87d5_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1002</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fd1e9d85e90>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2FN_u_3tnAp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "5ff1bfea-2e76-48f6-ddb9-cbc8d3d24093"
      },
      "source": [
        "x = [i for i in range(1,len(cum_reward)+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, cum_reward, 'r', label='Average Cumulative Reward')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('reward')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"Average Cumulative Reward per episode vs Iterations\")\n",
        "fig.savefig('CumReward_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5Xk_sditm95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "32e7478e-251e-4070-aee0-03acec95311a"
      },
      "source": [
        "plot_policy(POLICY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZjsfBUNtm7S"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}