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>
122 lines
5.0 KiB
Python
122 lines
5.0 KiB
Python
import os
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import gym
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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.env import SubprocVectorEnv
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from tianshou.utils.net.common import Net
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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 tianshou.policy import DiscreteSACPolicy
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from tianshou.utils.net.discrete import Actor, Critic
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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='CartPole-v0')
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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('--actor-lr', type=float, default=3e-4)
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parser.add_argument('--critic-lr', type=float, default=1e-3)
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parser.add_argument('--alpha-lr', type=float, default=3e-4)
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parser.add_argument('--gamma', type=float, default=0.95)
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parser.add_argument('--tau', type=float, default=0.005)
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parser.add_argument('--alpha', type=float, default=0.05)
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parser.add_argument('--auto_alpha', type=int, default=0)
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parser.add_argument('--epoch', type=int, default=5)
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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=5)
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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])
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parser.add_argument('--training-num', type=int, default=16)
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parser.add_argument('--test-num', type=int, default=100)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.0)
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parser.add_argument('--rew-norm', type=int, default=0)
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parser.add_argument('--ignore-done', type=int, 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_discrete_sac(args=get_args()):
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env = gym.make(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.action_space.shape or env.action_space.n
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train_envs = SubprocVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.training_num)])
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test_envs = SubprocVectorEnv(
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[lambda: gym.make(args.task) 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, softmax_output=False).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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net_c1 = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
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device=args.device)
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critic1 = Critic(net_c1, last_size=args.action_shape).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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net_c2 = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
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device=args.device)
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critic2 = Critic(net_c2, last_size=args.action_shape).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
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# better not to use auto alpha in CartPole
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if args.auto_alpha:
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target_entropy = 0.98 * np.log(np.prod(args.action_shape))
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log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
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alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
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args.alpha = (target_entropy, log_alpha, alpha_optim)
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policy = DiscreteSACPolicy(
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actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
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args.tau, args.gamma, args.alpha,
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reward_normalization=args.rew_norm,
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ignore_done=args.ignore_done)
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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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test_collector = Collector(policy, test_envs)
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# train_collector.collect(n_step=args.buffer_size)
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# log
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log_path = os.path.join(args.logdir, args.task, 'discrete_sac')
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writer = SummaryWriter(log_path)
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def save_fn(policy):
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torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
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def stop_fn(mean_rewards):
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return mean_rewards >= env.spec.reward_threshold
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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, stop_fn=stop_fn, save_fn=save_fn, writer=writer,
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test_in_train=False)
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assert stop_fn(result['best_reward'])
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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 = gym.make(args.task)
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policy.eval()
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collector = Collector(policy, env)
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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_discrete_sac()
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