maddpg_agent.py

import numpy as np
import random
import copy
from collections import namedtuple, deque

from model import Actor, Critic

import torch
import torch.nn.functional as F
import torch.optim as optim

BUFFER_SIZE = int(1e5)  # replay buffer size
BATCH_SIZE = 256        # minibatch size
LEARN_NUM = 2           # number of learning passes
GAMMA = 0.99            # discount factor
TAU = 1e-2              # for soft update of target parameters
LR_ACTOR = 1e-3         # learning rate of the actor 
LR_CRITIC = 1e-3        # learning rate of the critic
EPS_START = 5.0         # initial value for epsilon in noise decay process in Agent.act()
EPS_EP_END = 300        # episode to end the noise decay process
EPS_FINAL = 0           # final value for epsilon after decay

# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #BUG: torch==0.4.0 doesn't support newest graphics cards / drivers
# See https://stackoverflow.com/questions/60987997/why-torch-cuda-is-available-returns-false-even-after-installing-pytorch-with
device = torch.device("cpu") # >> Use CPU-only for now


class Agent():
    """Interacts with and learns from the environment."""

    def __init__(self, state_size, action_size, num_agents, random_seed):
        """Initialize an Agent object.

        Params
        ======
            state_size (int): dimension of each state
            action_size (int): dimension of each action
            num_agents (int): number of agents
            random_seed (int): random seed
        """
        self.state_size = state_size
        self.action_size = action_size
        self.num_agents = num_agents
        self.seed = random.seed(random_seed)
        self.eps = EPS_START
        self.eps_decay = 1/(EPS_EP_END*LEARN_NUM)  # set decay rate based on epsilon end target

        # Actor Network (w/ Target Network)
        self.actor_local = Actor(state_size, action_size, random_seed).to(device)
        self.actor_target = Actor(state_size, action_size, random_seed).to(device)
        self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=LR_ACTOR)

        # Critic Network (w/ Target Network)
        self.critic_local = Critic(state_size, action_size, random_seed).to(device)
        self.critic_target = Critic(state_size, action_size, random_seed).to(device)
        self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC)

        # Noise process
        self.noise = OUNoise((num_agents, action_size), random_seed)

         # Noise ampliture
        self.noise_amplitude = 1.0

        # Replay memory
        self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)

    def step(self, state, action, reward, next_state, done, agent_number):
        """Save experience in replay memory, and use random sample from buffer to learn."""
        # Save experience / reward
        self.memory.add(state, action, reward, next_state, done)

        # Learn, if enough samples are available in memory
        if len(self.memory) > BATCH_SIZE:
            for _ in range(LEARN_NUM):
                experiences = self.memory.sample()
                self.learn(experiences, GAMMA, agent_number)

    def act(self, state, add_noise):
        """Returns actions for both agents as per current policy, given their respective states."""
        states = torch.from_numpy(state).float().to(device)
        actions = np.zeros((self.num_agents, self.action_size))
        self.actor_local.eval()
        with torch.no_grad():
            # get action for each agent and concatenate them
            for agent_num, state in enumerate(states):
                action = self.actor_local(state).cpu().data.numpy()
                actions[agent_num, :] = action
        self.actor_local.train()
        if add_noise:
            actions += self.eps * self.noise.sample()
        return np.clip(actions, -1, 1)

    def reset(self):
        self.noise.reset()

    def learn(self, experiences, gamma, agent_number):
        """Update policy and value parameters using given batch of experience tuples.
        Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
        where:
            actor_target(state) -> action
            critic_target(state, action) -> Q-value

        Params
        ======
            experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples 
            gamma (float): discount factor
        """
        states, actions, rewards, next_states, dones = experiences

        # ---------------------------- update critic ---------------------------- #
        # Get predicted next-state actions and Q values from target models
        actions_next = self.actor_target(next_states)
        # Construct next actions vector relative to the agent
        if agent_number == 0:
            actions_next = torch.cat((actions_next, actions[:,2:]), dim=1)
        else:
            actions_next = torch.cat((actions[:,:2], actions_next), dim=1)
        # Compute Q targets for current states (y_i)
        Q_targets_next = self.critic_target(next_states, actions_next)
        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
        # Compute critic loss
        Q_expected = self.critic_local(states, actions)
        critic_loss = F.mse_loss(Q_expected, Q_targets)
        # Minimize the loss
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        
        # Gradient clipping
        torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
        self.critic_optimizer.step()

        # ---------------------------- update actor ---------------------------- #
        # Compute actor loss
        actions_pred = self.actor_local(states)
        # Construct action prediction vector relative to each agent
        if agent_number == 0:
            actions_pred = torch.cat((actions_pred, actions[:,2:]), dim=1)
        else:
            actions_pred = torch.cat((actions[:,:2], actions_pred), dim=1)
        # Compute actor loss
        actor_loss = -self.critic_local(states, actions_pred).mean()
        # Minimize the loss
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        # ----------------------- update target networks ----------------------- #
        self.soft_update(self.critic_local, self.critic_target, TAU)
        self.soft_update(self.actor_local, self.actor_target, TAU)
        # update noise decay parameter
        self.eps -= self.eps_decay
        self.eps = max(self.eps, EPS_FINAL)
        self.noise.reset()

    def soft_update(self, local_model, target_model, tau):
        """Soft update model parameters.
        θ_target = τ*θ_local + (1 - τ)*θ_target

        Params
        ======
            local_model: PyTorch model (weights will be copied from)
            target_model: PyTorch model (weights will be copied to)
            tau (float): interpolation parameter 
        """
        for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
            target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)


class OUNoise:
    """Ornstein-Uhlenbeck process."""

    def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2):
        """Initialize parameters and noise process."""
        
        self.size = size
        self.mu = mu * np.ones(size)
        self.theta = theta
        self.sigma = sigma
        self.seed = random.seed(seed)
        self.reset()

    def reset(self):
        """Reset the internal state (= noise) to mean (mu)."""
        self.state = copy.copy(self.mu)

    def sample(self):
        """Update internal state and return it as a noise sample."""
        x = self.state
        dx = self.theta * (self.mu - x) + self.sigma * np.random.standard_normal(self.size)
        self.state = x + dx
        return self.state


class ReplayBuffer:
    """Fixed-size buffer to store experience tuples."""

    def __init__(self, action_size, buffer_size, batch_size, seed):
        """Initialize a ReplayBuffer object.
        Params
        ======
            buffer_size (int): maximum size of buffer
            batch_size (int): size of each training batch
        """
        self.action_size = action_size
        self.memory = deque(maxlen=buffer_size)  # internal memory (deque)
        self.batch_size = batch_size
        self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
        self.seed = random.seed(seed)
    
    def add(self, state, action, reward, next_state, done):
        """Add a new experience to memory."""
        e = self.experience(state, action, reward, next_state, done)
        self.memory.append(e)
    
    def sample(self):
        """Randomly sample a batch of experiences from memory."""
        experiences = random.sample(self.memory, k=self.batch_size)

        states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).float().to(device)
        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)
        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)

        return (states, actions, rewards, next_states, dones)

    def __len__(self):
        """Return the current size of internal memory."""
        return len(self.memory)