Tianshou/examples/atari/atari_cql.py

import argparse
import datetime
import os
import pickle
import pprint

import numpy as np
import torch
from atari_network import QRDQN
from atari_wrapper import wrap_deepmind
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import ShmemVectorEnv
from tianshou.policy import DiscreteCQLPolicy
from tianshou.trainer import offline_trainer
from tianshou.utils import TensorboardLogger


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=0.0001)
    parser.add_argument("--gamma", type=float, default=0.99)
    parser.add_argument('--num-quantiles', type=int, default=200)
    parser.add_argument("--n-step", type=int, default=1)
    parser.add_argument("--target-update-freq", type=int, default=500)
    parser.add_argument("--min-q-weight", type=float, default=10.)
    parser.add_argument("--epoch", type=int, default=100)
    parser.add_argument("--update-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=10)
    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=100)
    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_cql(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 = ShmemVectorEnv(
        [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
    net = QRDQN(*args.state_shape, args.action_shape, args.num_quantiles, args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    # define policy
    policy = DiscreteCQLPolicy(
        net,
        optim,
        args.gamma,
        args.num_quantiles,
        args.n_step,
        args.target_update_freq,
        min_q_weight=args.min_q_weight
    ).to(args.device)
    # 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_qrdqn.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 = VectorReplayBuffer.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, exploration_noise=True)

    # log
    log_path = os.path.join(
        args.logdir, args.task, 'cql',
        f'seed_{args.seed}_{datetime.datetime.now().strftime("%m%d-%H%M%S")}'
    )
    writer = SummaryWriter(log_path)
    writer.add_text("args", str(args))
    logger = TensorboardLogger(writer, update_interval=args.log_interval)

    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=args.test_num, render=args.render)
        pprint.pprint(result)
        rew = result["rews"].mean()
        print(f'Mean reward (over {result["n/ep"]} episodes): {rew}')

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

    result = offline_trainer(
        policy,
        buffer,
        test_collector,
        args.epoch,
        args.update_per_epoch,
        args.test_num,
        args.batch_size,
        stop_fn=stop_fn,
        save_fn=save_fn,
        logger=logger
    )

    pprint.pprint(result)
    watch()


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