train_iqn.py

import argparse
import json
import os

import numpy as np
import torch
from torch import nn

import pfrl
from pfrl import experiments, explorers, replay_buffers, utils
from pfrl.wrappers import atari_wrappers


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--env", type=str, default="BreakoutNoFrameskip-v4")
    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)
    parser.add_argument("--demo", action="store_true", default=False)
    parser.add_argument("--load-pretrained", action="store_true", default=False)
    parser.add_argument(
        "--pretrained-type", type=str, default="best", choices=["best", "final"]
    )
    parser.add_argument("--load", type=str, default=None)
    parser.add_argument("--final-exploration-frames", type=int, default=10**6)
    parser.add_argument("--final-epsilon", type=float, default=0.01)
    parser.add_argument("--eval-epsilon", type=float, default=0.001)
    parser.add_argument("--steps", type=int, default=5 * 10**7)
    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)
    parser.add_argument("--target-update-interval", type=int, default=10**4)
    parser.add_argument("--eval-interval", type=int, default=250000)
    parser.add_argument("--eval-n-steps", type=int, default=125000)
    parser.add_argument("--update-interval", type=int, default=4)
    parser.add_argument("--batch-size", type=int, default=32)
    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(
        "--batch-accumulator", type=str, default="mean", choices=["mean", "sum"]
    )
    parser.add_argument("--quantile-thresholds-N", type=int, default=64)
    parser.add_argument("--quantile-thresholds-N-prime", type=int, default=64)
    parser.add_argument("--quantile-thresholds-K", type=int, default=32)
    parser.add_argument("--n-best-episodes", type=int, default=200)
    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 = pfrl.agents.iqn.ImplicitQuantileQFunction(
        psi=nn.Sequential(
            nn.Conv2d(4, 32, 8, stride=4),
            nn.ReLU(),
            nn.Conv2d(32, 64, 4, stride=2),
            nn.ReLU(),
            nn.Conv2d(64, 64, 3, stride=1),
            nn.ReLU(),
            nn.Flatten(),
        ),
        phi=nn.Sequential(
            pfrl.agents.iqn.CosineBasisLinear(64, 3136),
            nn.ReLU(),
        ),
        f=nn.Sequential(
            nn.Linear(3136, 512),
            nn.ReLU(),
            nn.Linear(512, n_actions),
        ),
    )

    # Use the same hyper parameters as https://arxiv.org/abs/1710.10044
    opt = torch.optim.Adam(q_func.parameters(), lr=5e-5, eps=1e-2 / args.batch_size)

    rbuf = replay_buffers.ReplayBuffer(10**6)

    explorer = explorers.LinearDecayEpsilonGreedy(
        1.0,
        args.final_epsilon,
        args.final_exploration_frames,
        lambda: np.random.randint(n_actions),
    )

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

    agent = pfrl.agents.IQN(
        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,
        update_interval=args.update_interval,
        batch_accumulator=args.batch_accumulator,
        phi=phi,
        quantile_thresholds_N=args.quantile_thresholds_N,
        quantile_thresholds_N_prime=args.quantile_thresholds_N_prime,
        quantile_thresholds_K=args.quantile_thresholds_K,
    )

    if args.load or args.load_pretrained:
        # either load or load_pretrained must be false
        assert not args.load or not args.load_pretrained
        if args.load:
            agent.load(args.load)
        else:
            agent.load(
                utils.download_model("IQN", args.env, model_type=args.pretrained_type)[
                    0
                ]
            )

    if args.demo:
        eval_stats = experiments.eval_performance(
            env=eval_env,
            agent=agent,
            n_steps=args.eval_n_steps,
            n_episodes=None,
        )
        print(
            "n_steps: {} mean: {} median: {} stdev {}".format(
                args.eval_n_steps,
                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=args.eval_n_steps,
            eval_n_episodes=None,
            eval_interval=args.eval_interval,
            outdir=args.outdir,
            save_best_so_far_agent=True,
            eval_env=eval_env,
        )

        dir_of_best_network = os.path.join(args.outdir, "best")
        agent.load(dir_of_best_network)

        # run 200 evaluation episodes, each capped at 30 mins of play
        stats = experiments.evaluator.eval_performance(
            env=eval_env,
            agent=agent,
            n_steps=None,
            n_episodes=args.n_best_episodes,
            max_episode_len=args.max_frames / 4,
            logger=None,
        )
        with open(os.path.join(args.outdir, "bestscores.json"), "w") as f:
            json.dump(stats, f)
        print("The results of the best scoring network:")
        for stat in stats:
            print(str(stat) + ":" + str(stats[stat]))


if __name__ == "__main__":
    main()