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.
113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
import os
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import gym
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import torch
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import pickle
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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.data import Collector
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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.trainer import offline_trainer
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from tianshou.policy import DiscreteBCQPolicy
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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("--eps-test", type=float, default=0.001)
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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("--n-step", type=int, default=3)
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parser.add_argument("--target-update-freq", type=int, default=320)
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parser.add_argument("--unlikely-action-threshold", type=float, default=0.3)
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parser.add_argument("--imitation-logits-penalty", type=float, default=0.01)
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parser.add_argument("--epoch", type=int, default=5)
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parser.add_argument("--update-per-epoch", type=int, default=1000)
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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("--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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"--load-buffer-name", type=str,
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default="./expert_DQN_CartPole-v0.pkl",
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)
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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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)
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args = parser.parse_known_args()[0]
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return args
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def test_discrete_bcq(args=get_args()):
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# envs
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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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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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test_envs.seed(args.seed)
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# model
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policy_net = Net(
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args.state_shape, args.action_shape,
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hidden_sizes=args.hidden_sizes, device=args.device).to(args.device)
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imitation_net = Net(
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args.state_shape, args.action_shape,
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hidden_sizes=args.hidden_sizes, device=args.device).to(args.device)
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optim = torch.optim.Adam(
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set(policy_net.parameters()).union(imitation_net.parameters()),
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lr=args.lr)
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policy = DiscreteBCQPolicy(
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policy_net, imitation_net, optim, args.gamma, args.n_step,
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args.target_update_freq, args.eps_test,
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args.unlikely_action_threshold, args.imitation_logits_penalty,
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)
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# buffer
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assert os.path.exists(args.load_buffer_name), \
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"Please run test_dqn.py first to get expert's data buffer."
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buffer = pickle.load(open(args.load_buffer_name, "rb"))
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# collector
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test_collector = Collector(policy, test_envs, exploration_noise=True)
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log_path = os.path.join(args.logdir, args.task, 'discrete_bcq')
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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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result = offline_trainer(
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policy, buffer, test_collector,
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args.epoch, args.update_per_epoch, args.test_num, args.batch_size,
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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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policy.eval()
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policy.set_eps(args.eps_test)
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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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if __name__ == "__main__":
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test_discrete_bcq(get_args())
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