77068af526
* update atari.py * fix setup.py pass the pytest * fix setup.py pass the pytest * add args "render" * change the tensorboard writter * change the tensorboard writter * change device, render, tensorboard log location * change device, render, tensorboard log location * remove some wrong local files * fix some tab mistakes and the envs name in continuous/test_xx.py * add examples and point robot maze environment * fix some bugs during testing examples * add dqn network and fix some args * change back the tensorboard writter's frequency to ensure ppo and a2c can write things normally * add a warning to collector * rm some unrelated files * reformat * fix a bug in test_dqn due to the model wrong selection
113 lines
4.4 KiB
Python
113 lines
4.4 KiB
Python
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.policy import PPOPolicy
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from tianshou.env import SubprocVectorEnv
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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.env.atari import create_atari_environment
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if __name__ == '__main__':
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from discrete_net import Net, Actor, Critic
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else: # pytest
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from test.discrete.net import Net, 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='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=1e-3)
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parser.add_argument('--gamma', type=float, default=0.99)
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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=100)
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parser.add_argument('--repeat-per-collect', type=int, default=2)
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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=1)
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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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# ppo 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=0.0)
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parser.add_argument('--eps-clip', type=float, default=0.2)
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parser.add_argument('--max-grad-norm', type=float, default=0.5)
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parser.add_argument('--max_episode_steps', type=int, default=2000)
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args = parser.parse_known_args()[0]
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return args
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def test_ppo(args=get_args()):
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env = create_atari_environment(args.task, max_episode_steps=args.max_episode_steps)
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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 = gym.make(args.task)
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train_envs = SubprocVectorEnv(
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[lambda: create_atari_environment(args.task, max_episode_steps=args.max_episode_steps) for _ in
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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, max_episode_steps=args.max_episode_steps) for _ in
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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.layer_num, args.state_shape, 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(list(
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actor.parameters()) + list(critic.parameters()), lr=args.lr)
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dist = torch.distributions.Categorical
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policy = PPOPolicy(
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actor, critic, optim, dist, args.gamma,
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max_grad_norm=args.max_grad_norm,
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eps_clip=args.eps_clip,
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vf_coef=args.vf_coef,
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ent_coef=args.ent_coef,
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action_range=None)
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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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# log
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writer = SummaryWriter(args.logdir + '/' + 'ppo')
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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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# 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, task=args.task)
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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)
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result = collector.collect(n_step=2000, 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_ppo()
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