a633a6a028
This is the first commit of 6 commits mentioned in #274, which features 1. Refactor of `Class Net` to support any form of MLP. 2. Enable type check in utils.network. 3. Relative change in docs/test/examples. 4. Move atari-related network to examples/atari/atari_network.py Co-authored-by: Trinkle23897 <trinkle23897@gmail.com>
106 lines
4.2 KiB
Python
106 lines
4.2 KiB
Python
import os
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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 A2CPolicy
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from tianshou.env import SubprocVectorEnv
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from tianshou.utils.net.common import Net
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from tianshou.trainer import onpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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from tianshou.utils.net.discrete import Actor, Critic
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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('--buffer-size', type=int, default=20000)
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parser.add_argument('--lr', type=float, default=3e-4)
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parser.add_argument('--gamma', type=float, default=0.9)
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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('--repeat-per-collect', type=int, default=1)
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parser.add_argument('--batch-size', type=int, default=64)
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parser.add_argument('--hidden-sizes', type=int,
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nargs='*', default=[128, 128, 128])
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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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# a2c special
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parser.add_argument('--vf-coef', type=float, default=0.5)
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parser.add_argument('--ent-coef', type=float, default=0.001)
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parser.add_argument('--max-grad-norm', type=float, default=None)
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parser.add_argument('--max-episode-steps', type=int, default=2000)
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return parser.parse_args()
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def test_a2c(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 = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
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device=args.device)
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actor = Actor(net, args.action_shape).to(args.device)
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critic = Critic(net).to(args.device)
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optim = torch.optim.Adam(set(
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actor.parameters()).union(critic.parameters()), lr=args.lr)
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dist = torch.distributions.Categorical
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policy = A2CPolicy(
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actor, critic, optim, dist, args.gamma, vf_coef=args.vf_coef,
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ent_coef=args.ent_coef, max_grad_norm=args.max_grad_norm)
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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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# log
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writer = SummaryWriter(os.path.join(args.logdir, args.task, 'a2c'))
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def stop_fn(mean_rewards):
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if env.env.spec.reward_threshold:
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return mean_rewards >= env.spec.reward_threshold
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else:
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return False
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# trainer
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result = onpolicy_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.repeat_per_collect,
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args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer)
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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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if __name__ == '__main__':
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test_a2c()
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