7036073649
This PR focus on some definition change of trainer to make it more friendly to use and be consistent with typical usage in research papers, typically change `collect-per-step` to `step-per-collect`, add `update-per-step` / `episode-per-collect` accordingly, and modify the documentation.
148 lines
6.2 KiB
Python
148 lines
6.2 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 DummyVectorEnv
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from tianshou.utils.net.common import Net
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.utils.net.discrete import Actor, Critic
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from tianshou.policy import A2CPolicy, ImitationPolicy
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from tianshou.trainer import onpolicy_trainer, offpolicy_trainer
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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=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=3e-4)
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parser.add_argument('--il-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('--epoch', type=int, default=10)
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parser.add_argument('--step-per-epoch', type=int, default=50000)
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parser.add_argument('--il-step-per-epoch', type=int, default=1000)
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parser.add_argument('--episode-per-collect', type=int, default=8)
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parser.add_argument('--step-per-collect', type=int, default=8)
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parser.add_argument('--update-per-step', type=float, default=0.125)
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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('--imitation-hidden-sizes', type=int,
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nargs='*', default=[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=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.)
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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=0.0)
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parser.add_argument('--max-grad-norm', type=float, default=None)
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parser.add_argument('--gae-lambda', type=float, default=1.)
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parser.add_argument('--rew-norm', type=bool, default=False)
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args = parser.parse_known_args()[0]
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return args
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def test_a2c_with_il(args=get_args()):
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torch.set_num_threads(1) # for poor CPU
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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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# you can also use tianshou.env.SubprocVectorEnv
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# train_envs = gym.make(args.task)
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train_envs = DummyVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.training_num)])
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# test_envs = gym.make(args.task)
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test_envs = DummyVectorEnv(
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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, device=args.device).to(args.device)
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critic = Critic(net, device=args.device).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, gae_lambda=args.gae_lambda,
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vf_coef=args.vf_coef, ent_coef=args.ent_coef,
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max_grad_norm=args.max_grad_norm, reward_normalization=args.rew_norm)
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# collector
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train_collector = Collector(
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policy, train_envs,
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VectorReplayBuffer(args.buffer_size, len(train_envs)),
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exploration_noise=True)
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test_collector = Collector(policy, test_envs)
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# log
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log_path = os.path.join(args.logdir, args.task, 'a2c')
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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 = onpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.repeat_per_collect, args.test_num, args.batch_size,
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episode_per_collect=args.episode_per_collect, stop_fn=stop_fn, save_fn=save_fn,
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writer=writer)
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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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rews, lens = result["rews"], result["lens"]
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print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
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policy.eval()
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# here we define an imitation collector with a trivial policy
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if args.task == 'CartPole-v0':
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env.spec.reward_threshold = 190 # lower the goal
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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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net = Actor(net, args.action_shape, device=args.device).to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.il_lr)
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il_policy = ImitationPolicy(net, optim, mode='discrete')
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il_test_collector = Collector(
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il_policy,
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DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)])
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)
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train_collector.reset()
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result = offpolicy_trainer(
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il_policy, train_collector, il_test_collector, args.epoch,
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args.il_step_per_epoch, args.step_per_collect, args.test_num,
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args.batch_size, stop_fn=stop_fn, save_fn=save_fn, writer=writer)
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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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il_policy.eval()
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collector = Collector(il_policy, env)
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result = collector.collect(n_episode=1, render=args.render)
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rews, lens = result["rews"], result["lens"]
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print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
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if __name__ == '__main__':
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test_a2c_with_il()
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