148 lines
6.1 KiB
Python
Executable File
148 lines
6.1 KiB
Python
Executable File
#!/usr/bin/env python3
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import os
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import gym
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import torch
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import datetime
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import argparse
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import numpy as np
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from torch import nn
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from torch.optim.lr_scheduler import LambdaLR
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from torch.utils.tensorboard import SummaryWriter
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from torch.distributions import Independent, Normal
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from tianshou.policy import PGPolicy
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from tianshou.utils import BasicLogger
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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 onpolicy_trainer
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from tianshou.utils.net.continuous import ActorProb
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from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer
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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='HalfCheetah-v3')
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parser.add_argument('--seed', type=int, default=0)
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parser.add_argument('--buffer-size', type=int, default=4096)
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parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
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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=30000)
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parser.add_argument('--step-per-collect', type=int, default=2048)
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parser.add_argument('--repeat-per-collect', type=int, default=1)
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# batch-size >> step-per-collect means caculating all data in one singe forward.
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parser.add_argument('--batch-size', type=int, default=99999)
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parser.add_argument('--training-num', type=int, default=64)
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parser.add_argument('--test-num', type=int, default=10)
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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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parser.add_argument('--resume-path', type=str, default=None)
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# reinforce special
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parser.add_argument('--rew-norm', type=int, default=True)
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# "clip" option also works well.
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parser.add_argument('--action-bound-method', type=str, default="tanh")
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parser.add_argument('--lr-decay', type=int, default=True)
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return parser.parse_args()
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def test_reinforce(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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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Action range:", np.min(env.action_space.low),
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np.max(env.action_space.high))
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# train_envs = gym.make(args.task)
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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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norm_obs=True)
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# test_envs = gym.make(args.task)
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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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norm_obs=True, obs_rms=train_envs.obs_rms, update_obs_rms=False)
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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_a = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
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activation=nn.Tanh, device=args.device)
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actor = ActorProb(net_a, args.action_shape, max_action=args.max_action,
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unbounded=True, device=args.device).to(args.device)
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torch.nn.init.constant_(actor.sigma_param, -0.5)
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for m in actor.modules():
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if isinstance(m, torch.nn.Linear):
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# orthogonal initialization
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torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2))
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torch.nn.init.zeros_(m.bias)
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# do last policy layer scaling, this will make initial actions have (close to)
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# 0 mean and std, and will help boost performances,
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# see https://arxiv.org/abs/2006.05990, Fig.24 for details
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for m in actor.mu.modules():
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if isinstance(m, torch.nn.Linear):
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torch.nn.init.zeros_(m.bias)
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m.weight.data.copy_(0.01 * m.weight.data)
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optim = torch.optim.Adam(actor.parameters(), lr=args.lr)
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lr_scheduler = None
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if args.lr_decay:
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# decay learning rate to 0 linearly
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max_update_num = np.ceil(
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args.step_per_epoch / args.step_per_collect) * args.epoch
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lr_scheduler = LambdaLR(
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optim, lr_lambda=lambda epoch: 1 - epoch / max_update_num)
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def dist(*logits):
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return Independent(Normal(*logits), 1)
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policy = PGPolicy(actor, optim, dist, discount_factor=args.gamma,
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reward_normalization=args.rew_norm, action_scaling=True,
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action_bound_method=args.action_bound_method,
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lr_scheduler=lr_scheduler, action_space=env.action_space)
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# collector
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if args.training_num > 1:
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buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
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else:
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buffer = ReplayBuffer(args.buffer_size)
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train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
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test_collector = Collector(policy, test_envs)
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# log
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t0 = datetime.datetime.now().strftime("%m%d_%H%M%S")
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log_file = f'seed_{args.seed}_{t0}-{args.task.replace("-", "_")}_reinforce'
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log_path = os.path.join(args.logdir, args.task, 'reinforce', log_file)
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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logger = BasicLogger(writer, update_interval=10, train_interval=100)
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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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# trainer
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result = onpolicy_trainer(
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policy, train_collector, test_collector, args.epoch, args.step_per_epoch,
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args.repeat_per_collect, args.test_num, args.batch_size,
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step_per_collect=args.step_per_collect, save_fn=save_fn, logger=logger,
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test_in_train=False)
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# Let's watch its performance!
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policy.eval()
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test_envs.seed(args.seed)
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test_collector.reset()
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result = test_collector.collect(n_episode=args.test_num, render=args.render)
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print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')
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if __name__ == '__main__':
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test_reinforce()
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