bd9c3c7f8d
* pre * update docs * update docs * $ in bash * size -> hidden_layer_size * doctest * doctest again * filter a warning * fix bug * fix examples * test fail * test succ
113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
import torch
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import pprint
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import argparse
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.policy import DQNPolicy
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from tianshou.env import SubprocVectorEnv
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from tianshou.utils.net.discrete import DQN
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from tianshou.trainer import offpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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from atari import create_atari_environment, preprocess_fn
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--task', type=str, default='Pong')
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parser.add_argument('--seed', type=int, default=1626)
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parser.add_argument('--eps-test', type=float, default=0.05)
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parser.add_argument('--eps-train', type=float, default=0.1)
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parser.add_argument('--buffer-size', type=int, default=20000)
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parser.add_argument('--lr', type=float, default=1e-3)
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parser.add_argument('--gamma', type=float, default=0.9)
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parser.add_argument('--n-step', type=int, default=1)
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parser.add_argument('--target-update-freq', type=int, default=320)
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parser.add_argument('--epoch', type=int, default=100)
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parser.add_argument('--step-per-epoch', type=int, default=1000)
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parser.add_argument('--collect-per-step', type=int, default=10)
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parser.add_argument('--batch-size', type=int, default=64)
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parser.add_argument('--layer-num', type=int, default=3)
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parser.add_argument('--training-num', type=int, default=8)
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parser.add_argument('--test-num', type=int, default=8)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.)
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parser.add_argument(
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'--device', type=str,
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default='cuda' if torch.cuda.is_available() else 'cpu')
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args = parser.parse_known_args()[0]
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return args
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def test_dqn(args=get_args()):
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env = create_atari_environment(args.task)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.env.action_space.shape or env.env.action_space.n
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# train_envs = gym.make(args.task)
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train_envs = SubprocVectorEnv([
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lambda: create_atari_environment(args.task)
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for _ in range(args.training_num)])
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv([
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lambda: create_atari_environment(args.task)
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for _ in range(args.test_num)])
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# model
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net = DQN(
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args.state_shape[0], args.state_shape[1],
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args.action_shape, args.device)
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net = net.to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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policy = DQNPolicy(
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net, optim, args.gamma, args.n_step,
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target_update_freq=args.target_update_freq)
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# collector
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train_collector = Collector(
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policy, train_envs, ReplayBuffer(args.buffer_size),
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preprocess_fn=preprocess_fn)
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test_collector = Collector(policy, test_envs, preprocess_fn=preprocess_fn)
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# policy.set_eps(1)
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train_collector.collect(n_step=args.batch_size * 4)
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print(len(train_collector.buffer))
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# log
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writer = SummaryWriter(args.logdir + '/' + 'dqn')
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def stop_fn(x):
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if env.env.spec.reward_threshold:
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return x >= env.spec.reward_threshold
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else:
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return False
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def train_fn(x):
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policy.set_eps(args.eps_train)
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def test_fn(x):
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policy.set_eps(args.eps_test)
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# trainer
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result = offpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.collect_per_step, args.test_num,
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args.batch_size, train_fn=train_fn, test_fn=test_fn,
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stop_fn=stop_fn, writer=writer)
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train_collector.close()
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test_collector.close()
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if __name__ == '__main__':
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pprint.pprint(result)
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# Let's watch its performance!
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env = create_atari_environment(args.task)
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collector = Collector(policy, env, preprocess_fn=preprocess_fn)
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result = collector.collect(n_episode=1, render=args.render)
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print(f'Final reward: {result["rew"]}, length: {result["len"]}')
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collector.close()
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if __name__ == '__main__':
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test_dqn(get_args())
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