train_dqn_ale.py

import argparse

import torch
import torch.nn as nn
import numpy as np

import pfrl
from pfrl.q_functions import DiscreteActionValueHead
from pfrl import agents
from pfrl import experiments
from pfrl import explorers
from pfrl import nn as pnn
from pfrl import utils
from pfrl.q_functions import DuelingDQN
from pfrl import replay_buffers

from pfrl.wrappers import atari_wrappers
from pfrl.initializers import init_chainer_default


class SingleSharedBias(nn.Module):
    """Single shared bias used in the Double DQN paper.

    You can add this link after a Linear layer with nobias=True to implement a
    Linear layer with a single shared bias parameter.

    See http://arxiv.org/abs/1509.06461.
    """

    def __init__(self):
        super().__init__()
        self.bias = nn.Parameter(torch.zeros([1], dtype=torch.float32))

    def __call__(self, x):
        return x + self.bias.expand_as(x)


def parse_arch(arch, n_actions):
    if arch == "nature":
        return nn.Sequential(
            pnn.LargeAtariCNN(),
            init_chainer_default(nn.Linear(512, n_actions)),
            DiscreteActionValueHead(),
        )
    elif arch == "doubledqn":
        return nn.Sequential(
            pnn.LargeAtariCNN(),
            init_chainer_default(nn.Linear(512, n_actions, bias=False)),
            SingleSharedBias(),
            DiscreteActionValueHead(),
        )
    elif arch == "nips":
        return nn.Sequential(
            pnn.SmallAtariCNN(),
            init_chainer_default(nn.Linear(256, n_actions)),
            DiscreteActionValueHead(),
        )
    elif arch == "dueling":
        return DuelingDQN(n_actions)
    else:
        raise RuntimeError("Not supported architecture: {}".format(arch))


def parse_agent(agent):
    return {"DQN": agents.DQN, "DoubleDQN": agents.DoubleDQN, "PAL": agents.PAL}[agent]


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--env",
        type=str,
        default="BreakoutNoFrameskip-v4",
        help="OpenAI Atari domain to perform algorithm on.",
    )
    parser.add_argument(
        "--outdir",
        type=str,
        default="results",
        help=(
            "Directory path to save output files."
            " If it does not exist, it will be created."
        ),
    )
    parser.add_argument("--seed", type=int, default=0, help="Random seed [0, 2 ** 31)")
    parser.add_argument(
        "--gpu", type=int, default=0, help="GPU to use, set to -1 if no GPU."
    )
    parser.add_argument("--demo", action="store_true", default=False)
    parser.add_argument("--load", type=str, default=None)
    parser.add_argument(
        "--final-exploration-frames",
        type=int,
        default=10 ** 6,
        help="Timesteps after which we stop " + "annealing exploration rate",
    )
    parser.add_argument(
        "--final-epsilon",
        type=float,
        default=0.01,
        help="Final value of epsilon during training.",
    )
    parser.add_argument(
        "--eval-epsilon",
        type=float,
        default=0.001,
        help="Exploration epsilon used during eval episodes.",
    )
    parser.add_argument("--noisy-net-sigma", type=float, default=None)
    parser.add_argument(
        "--arch",
        type=str,
        default="doubledqn",
        choices=["nature", "nips", "dueling", "doubledqn"],
        help="Network architecture to use.",
    )
    parser.add_argument(
        "--steps",
        type=int,
        default=5 * 10 ** 7,
        help="Total number of timesteps to train the agent.",
    )
    parser.add_argument(
        "--max-frames",
        type=int,
        default=30 * 60 * 60,  # 30 minutes with 60 fps
        help="Maximum number of frames for each episode.",
    )
    parser.add_argument(
        "--replay-start-size",
        type=int,
        default=5 * 10 ** 4,
        help="Minimum replay buffer size before " + "performing gradient updates.",
    )
    parser.add_argument(
        "--target-update-interval",
        type=int,
        default=3 * 10 ** 4,
        help="Frequency (in timesteps) at which " + "the target network is updated.",
    )
    parser.add_argument(
        "--eval-interval",
        type=int,
        default=10 ** 5,
        help="Frequency (in timesteps) of evaluation phase.",
    )
    parser.add_argument(
        "--update-interval",
        type=int,
        default=4,
        help="Frequency (in timesteps) of network updates.",
    )
    parser.add_argument("--eval-n-runs", type=int, default=10)
    parser.add_argument("--no-clip-delta", dest="clip_delta", action="store_false")
    parser.add_argument("--num-step-return", type=int, default=1)
    parser.set_defaults(clip_delta=True)
    parser.add_argument(
        "--agent", type=str, default="DoubleDQN", choices=["DQN", "DoubleDQN", "PAL"]
    )
    parser.add_argument(
        "--log-level",
        type=int,
        default=20,
        help="Logging level. 10:DEBUG, 20:INFO etc.",
    )
    parser.add_argument(
        "--render",
        action="store_true",
        default=False,
        help="Render env states in a GUI window.",
    )
    parser.add_argument(
        "--monitor",
        action="store_true",
        default=False,
        help=(
            "Monitor env. Videos and additional information are saved as output files."
        ),
    )
    parser.add_argument("--lr", type=float, default=2.5e-4, help="Learning rate.")
    parser.add_argument(
        "--prioritized",
        action="store_true",
        default=False,
        help="Use prioritized experience replay.",
    )
    parser.add_argument(
        "--checkpoint-frequency",
        type=int,
        default=None,
        help="Frequency at which agents are stored.",
    )
    args = parser.parse_args()

    import logging

    logging.basicConfig(level=args.log_level)

    # Set a random seed used in PFRL.
    utils.set_random_seed(args.seed)

    # Set different random seeds for train and test envs.
    train_seed = args.seed
    test_seed = 2 ** 31 - 1 - args.seed

    args.outdir = experiments.prepare_output_dir(args, args.outdir)
    print("Output files are saved in {}".format(args.outdir))

    def make_env(test):
        # Use different random seeds for train and test envs
        env_seed = test_seed if test else train_seed
        env = atari_wrappers.wrap_deepmind(
            atari_wrappers.make_atari(args.env, max_frames=args.max_frames),
            episode_life=not test,
            clip_rewards=not test,
        )
        env.seed(int(env_seed))
        if test:
            # Randomize actions like epsilon-greedy in evaluation as well
            env = pfrl.wrappers.RandomizeAction(env, args.eval_epsilon)
        if args.monitor:
            env = pfrl.wrappers.Monitor(
                env, args.outdir, mode="evaluation" if test else "training"
            )
        if args.render:
            env = pfrl.wrappers.Render(env)
        return env

    env = make_env(test=False)
    eval_env = make_env(test=True)

    n_actions = env.action_space.n
    q_func = parse_arch(args.arch, n_actions)

    if args.noisy_net_sigma is not None:
        pnn.to_factorized_noisy(q_func, sigma_scale=args.noisy_net_sigma)
        # Turn off explorer
        explorer = explorers.Greedy()
    else:
        explorer = explorers.LinearDecayEpsilonGreedy(
            1.0,
            args.final_epsilon,
            args.final_exploration_frames,
            lambda: np.random.randint(n_actions),
        )

    # Use the Nature paper's hyperparameters
    opt = pfrl.optimizers.RMSpropEpsInsideSqrt(
        q_func.parameters(),
        lr=args.lr,
        alpha=0.95,
        momentum=0.0,
        eps=1e-2,
        centered=True,
    )

    # Select a replay buffer to use
    if args.prioritized:
        # Anneal beta from beta0 to 1 throughout training
        betasteps = args.steps / args.update_interval
        rbuf = replay_buffers.PrioritizedReplayBuffer(
            10 ** 6,
            alpha=0.6,
            beta0=0.4,
            betasteps=betasteps,
            num_steps=args.num_step_return,
        )
    else:
        rbuf = replay_buffers.ReplayBuffer(10 ** 6, args.num_step_return)

    def phi(x):
        # Feature extractor
        return np.asarray(x, dtype=np.float32) / 255

    Agent = parse_agent(args.agent)
    agent = Agent(
        q_func,
        opt,
        rbuf,
        gpu=args.gpu,
        gamma=0.99,
        explorer=explorer,
        replay_start_size=args.replay_start_size,
        target_update_interval=args.target_update_interval,
        clip_delta=args.clip_delta,
        update_interval=args.update_interval,
        batch_accumulator="sum",
        phi=phi,
    )

    if args.load:
        agent.load(args.load)

    if args.demo:
        eval_stats = experiments.eval_performance(
            env=eval_env, agent=agent, n_steps=None, n_episodes=args.eval_n_runs
        )
        print(
            "n_runs: {} mean: {} median: {} stdev {}".format(
                args.eval_n_runs,
                eval_stats["mean"],
                eval_stats["median"],
                eval_stats["stdev"],
            )
        )
    else:
        experiments.train_agent_with_evaluation(
            agent=agent,
            env=env,
            steps=args.steps,
            eval_n_steps=None,
            checkpoint_freq=args.checkpoint_frequency,
            eval_n_episodes=args.eval_n_runs,
            eval_interval=args.eval_interval,
            outdir=args.outdir,
            save_best_so_far_agent=False,
            eval_env=eval_env,
        )


if __name__ == "__main__":
    main()