atari_dqn.py

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

from tianshou.policy import DQNPolicy
from tianshou.utils import BasicLogger
from tianshou.env import SubprocVectorEnv
from tianshou.trainer import offpolicy_trainer
from tianshou.data import Collector, VectorReplayBuffer

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=0)
    parser.add_argument('--eps-test', type=float, default=0.005)
    parser.add_argument('--eps-train', type=float, default=1.)
    parser.add_argument('--eps-train-final', type=float, default=0.05)
    parser.add_argument('--buffer-size', type=int, default=100000)
    parser.add_argument('--lr', type=float, default=0.0001)
    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=500)
    parser.add_argument('--epoch', type=int, default=100)
    parser.add_argument('--step-per-epoch', type=int, default=100000)
    parser.add_argument('--step-per-collect', type=int, default=10)
    parser.add_argument('--update-per-step', type=float, default=0.1)
    parser.add_argument('--batch-size', type=int, default=32)
    parser.add_argument('--training-num', type=int, default=10)
    parser.add_argument('--test-num', type=int, default=10)
    parser.add_argument('--logdir', type=str, default='log')
    parser.add_argument('--render', type=float, default=0.)
    parser.add_argument(
        '--device', type=str,
        default='cuda' if torch.cuda.is_available() else 'cpu')
    parser.add_argument('--frames-stack', type=int, default=4)
    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('--save-buffer-name', type=str, default=None)
    return parser.parse_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_dqn(args=get_args()):
    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
    train_envs = SubprocVectorEnv([lambda: make_atari_env(args)
                                   for _ in range(args.training_num)])
    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)
    train_envs.seed(args.seed)
    test_envs.seed(args.seed)
    # define model
    net = DQN(*args.state_shape,
              args.action_shape, args.device).to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    # define policy
    policy = DQNPolicy(net, optim, args.gamma, args.n_step,
                       target_update_freq=args.target_update_freq)
    # 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)
    # replay buffer: `save_last_obs` and `stack_num` can be removed together
    # when you have enough RAM
    buffer = VectorReplayBuffer(
        args.buffer_size, buffer_num=len(train_envs), ignore_obs_next=True,
        save_only_last_obs=True, stack_num=args.frames_stack)
    # collector
    train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
    test_collector = Collector(policy, test_envs, exploration_noise=True)
    # log
    log_path = os.path.join(args.logdir, args.task, 'dqn')
    writer = SummaryWriter(log_path)
    writer.add_text("args", str(args))
    logger = BasicLogger(writer)

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

    def stop_fn(mean_rewards):
        if env.spec.reward_threshold:
            return mean_rewards >= env.spec.reward_threshold
        elif 'Pong' in args.task:
            return mean_rewards >= 20
        else:
            return False

    def train_fn(epoch, env_step):
        # nature DQN setting, linear decay in the first 1M steps
        if env_step <= 1e6:
            eps = args.eps_train - env_step / 1e6 * \
                (args.eps_train - args.eps_train_final)
        else:
            eps = args.eps_train_final
        policy.set_eps(eps)
        logger.write('train/eps', env_step, eps)

    def test_fn(epoch, env_step):
        policy.set_eps(args.eps_test)

    # watch agent's performance
    def watch():
        print("Setup test envs ...")
        policy.eval()
        policy.set_eps(args.eps_test)
        test_envs.seed(args.seed)
        if args.save_buffer_name:
            print(f"Generate buffer with size {args.buffer_size}")
            buffer = VectorReplayBuffer(
                args.buffer_size, buffer_num=len(test_envs),
                ignore_obs_next=True, save_only_last_obs=True,
                stack_num=args.frames_stack)
            collector = Collector(policy, test_envs, buffer,
                                  exploration_noise=True)
            result = collector.collect(n_step=args.buffer_size)
            print(f"Save buffer into {args.save_buffer_name}")
            # Unfortunately, pickle will cause oom with 1M buffer size
            buffer.save_hdf5(args.save_buffer_name)
        else:
            print("Testing agent ...")
            test_collector.reset()
            result = test_collector.collect(n_episode=args.test_num,
                                            render=args.render)
        rew = result["rews"].mean()
        print(f'Mean reward (over {result["n/ep"]} episodes): {rew}')

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

    # test train_collector and start filling replay buffer
    train_collector.collect(n_step=args.batch_size * args.training_num)
    # trainer
    result = offpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.step_per_collect, args.test_num,
        args.batch_size, train_fn=train_fn, test_fn=test_fn,
        stop_fn=stop_fn, save_fn=save_fn, logger=logger,
        update_per_step=args.update_per_step, test_in_train=False)

    pprint.pprint(result)
    watch()


if __name__ == '__main__':
    test_dqn(get_args())