atari_bcq.py

import os
import torch
import pickle
import pprint
import argparse
import numpy as np
from torch.utils.tensorboard import SummaryWriter

from tianshou.env import SubprocVectorEnv
from tianshou.trainer import offline_trainer
from tianshou.utils.net.discrete import Actor
from tianshou.policy import DiscreteBCQPolicy
from tianshou.data import Collector, ReplayBuffer

from atari_network import DQN
from atari_wrapper import wrap_deepmind


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", type=str, default="PongNoFrameskip-v4")
    parser.add_argument("--seed", type=int, default=1626)
    parser.add_argument("--eps-test", type=float, default=0.001)
    parser.add_argument("--lr", type=float, default=6.25e-5)
    parser.add_argument("--gamma", type=float, default=0.99)
    parser.add_argument("--n-step", type=int, default=3)
    parser.add_argument("--target-update-freq", type=int, default=8000)
    parser.add_argument("--unlikely-action-threshold", type=float, default=0.3)
    parser.add_argument("--imitation-logits-penalty", type=float, default=0.01)
    parser.add_argument("--epoch", type=int, default=100)
    parser.add_argument("--step-per-epoch", type=int, default=10000)
    parser.add_argument("--batch-size", type=int, default=32)
    parser.add_argument('--hidden-sizes', type=int,
                        nargs='*', default=[512])
    parser.add_argument("--test-num", type=int, default=100)
    parser.add_argument('--frames_stack', type=int, default=4)
    parser.add_argument("--logdir", type=str, default="log")
    parser.add_argument("--render", type=float, default=0.)
    parser.add_argument("--resume-path", type=str, default=None)
    parser.add_argument("--watch", default=False, action="store_true",
                        help="watch the play of pre-trained policy only")
    parser.add_argument("--log-interval", type=int, default=1000)
    parser.add_argument(
        "--load-buffer-name", type=str,
        default="./expert_DQN_PongNoFrameskip-v4.hdf5",
    )
    parser.add_argument(
        "--device", type=str,
        default="cuda" if torch.cuda.is_available() else "cpu",
    )
    args = parser.parse_known_args()[0]
    return args


def make_atari_env(args):
    return wrap_deepmind(args.task, frame_stack=args.frames_stack)


def make_atari_env_watch(args):
    return wrap_deepmind(args.task, frame_stack=args.frames_stack,
                         episode_life=False, clip_rewards=False)


def test_discrete_bcq(args=get_args()):
    # envs
    env = make_atari_env(args)
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    # should be N_FRAMES x H x W
    print("Observations shape:", args.state_shape)
    print("Actions shape:", args.action_shape)
    # make environments
    test_envs = SubprocVectorEnv([lambda: make_atari_env_watch(args)
                                  for _ in range(args.test_num)])
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    test_envs.seed(args.seed)
    # model
    feature_net = DQN(*args.state_shape, args.action_shape,
                      device=args.device, features_only=True).to(args.device)
    policy_net = Actor(feature_net, args.action_shape,
                       hidden_sizes=args.hidden_sizes).to(args.device)
    imitation_net = Actor(feature_net, args.action_shape,
                          hidden_sizes=args.hidden_sizes).to(args.device)
    optim = torch.optim.Adam(
        set(policy_net.parameters()).union(imitation_net.parameters()),
        lr=args.lr,
    )
    # define policy
    policy = DiscreteBCQPolicy(
        policy_net, imitation_net, optim, args.gamma, args.n_step,
        args.target_update_freq, args.eps_test,
        args.unlikely_action_threshold, args.imitation_logits_penalty,
    )
    # load a previous policy
    if args.resume_path:
        policy.load_state_dict(torch.load(
            args.resume_path, map_location=args.device
        ))
        print("Loaded agent from: ", args.resume_path)
    # buffer
    assert os.path.exists(args.load_buffer_name), \
        "Please run atari_dqn.py first to get expert's data buffer."
    if args.load_buffer_name.endswith('.pkl'):
        buffer = pickle.load(open(args.load_buffer_name, "rb"))
    elif args.load_buffer_name.endswith('.hdf5'):
        buffer = ReplayBuffer.load_hdf5(args.load_buffer_name)
    else:
        print(f"Unknown buffer format: {args.load_buffer_name}")
        exit(0)

    # collector
    test_collector = Collector(policy, test_envs)

    log_path = os.path.join(args.logdir, args.task, 'discrete_bcq')
    writer = SummaryWriter(log_path)

    def save_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))

    def stop_fn(mean_rewards):
        return False

    # watch agent's performance
    def watch():
        print("Setup test envs ...")
        policy.eval()
        policy.set_eps(args.eps_test)
        test_envs.seed(args.seed)
        print("Testing agent ...")
        test_collector.reset()
        result = test_collector.collect(n_episode=[1] * args.test_num,
                                        render=args.render)
        pprint.pprint(result)

    if args.watch:
        watch()
        exit(0)

    result = offline_trainer(
        policy, buffer, test_collector,
        args.epoch, args.step_per_epoch, args.test_num, args.batch_size,
        stop_fn=stop_fn, save_fn=save_fn, writer=writer,
        log_interval=args.log_interval,
    )

    pprint.pprint(result)
    watch()


if __name__ == "__main__":
    test_discrete_bcq(get_args())