179 lines
6.3 KiB
Python
179 lines
6.3 KiB
Python
import argparse
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import os
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import pprint
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import gym
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import numpy as np
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.env import DummyVectorEnv
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from tianshou.policy import REDQPolicy
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from tianshou.trainer import offpolicy_trainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import EnsembleLinear, Net
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from tianshou.utils.net.continuous import ActorProb, 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='Pendulum-v1')
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parser.add_argument('--reward-threshold', type=float, default=None)
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parser.add_argument('--seed', type=int, default=0)
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parser.add_argument('--buffer-size', type=int, default=20000)
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parser.add_argument('--ensemble-size', type=int, default=4)
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parser.add_argument('--subset-size', type=int, default=2)
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parser.add_argument('--actor-lr', type=float, default=1e-4)
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parser.add_argument('--critic-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('--tau', type=float, default=0.005)
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parser.add_argument('--alpha', type=float, default=0.2)
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parser.add_argument('--auto-alpha', action='store_true', default=False)
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parser.add_argument('--alpha-lr', type=float, default=3e-4)
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parser.add_argument("--start-timesteps", type=int, default=1000)
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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=5000)
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parser.add_argument('--step-per-collect', type=int, default=1)
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parser.add_argument('--update-per-step', type=int, default=3)
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parser.add_argument('--n-step', type=int, default=1)
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parser.add_argument('--batch-size', type=int, default=64)
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parser.add_argument(
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'--target-mode', type=str, choices=('min', 'mean'), default='min'
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)
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parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
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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, 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_redq(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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args.max_action = env.action_space.high[0]
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if args.reward_threshold is None:
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default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250}
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args.reward_threshold = default_reward_threshold.get(
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args.task, env.spec.reward_threshold
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)
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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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)
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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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)
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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, device=args.device)
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actor = ActorProb(
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net,
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args.action_shape,
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max_action=args.max_action,
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device=args.device,
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unbounded=True,
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conditioned_sigma=True
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).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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def linear(x, y):
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return EnsembleLinear(args.ensemble_size, x, y)
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net_c = Net(
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args.state_shape,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device,
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linear_layer=linear,
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)
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critic = Critic(
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net_c, device=args.device, linear_layer=linear, flatten_input=False
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).to(args.device)
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critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
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if args.auto_alpha:
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target_entropy = -np.prod(env.action_space.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 = REDQPolicy(
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actor,
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actor_optim,
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critic,
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critic_optim,
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args.ensemble_size,
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args.subset_size,
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tau=args.tau,
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gamma=args.gamma,
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alpha=args.alpha,
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estimation_step=args.n_step,
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actor_delay=args.update_per_step,
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target_mode=args.target_mode,
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action_space=env.action_space,
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)
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# collector
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train_collector = Collector(
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policy,
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train_envs,
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VectorReplayBuffer(args.buffer_size, len(train_envs)),
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exploration_noise=True
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)
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test_collector = Collector(policy, test_envs)
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train_collector.collect(n_step=args.start_timesteps, random=True)
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# log
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log_path = os.path.join(args.logdir, args.task, 'redq')
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writer = SummaryWriter(log_path)
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logger = TensorboardLogger(writer)
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def save_best_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 >= args.reward_threshold
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# trainer
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result = offpolicy_trainer(
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policy,
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train_collector,
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test_collector,
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args.epoch,
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args.step_per_epoch,
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args.step_per_collect,
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args.test_num,
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args.batch_size,
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update_per_step=args.update_per_step,
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stop_fn=stop_fn,
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save_best_fn=save_best_fn,
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logger=logger
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)
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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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if __name__ == '__main__':
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test_redq()
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