a633a6a028
This is the first commit of 6 commits mentioned in #274, which features 1. Refactor of `Class Net` to support any form of MLP. 2. Enable type check in utils.network. 3. Relative change in docs/test/examples. 4. Move atari-related network to examples/atari/atari_network.py Co-authored-by: Trinkle23897 <trinkle23897@gmail.com>
151 lines
6.1 KiB
Python
151 lines
6.1 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.policy import SACPolicy
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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 offpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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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='Ant-v3')
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parser.add_argument('--seed', type=int, default=1626)
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parser.add_argument('--buffer-size', type=int, default=1000000)
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parser.add_argument('--actor-lr', type=float, default=3e-4)
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parser.add_argument('--critic-lr', type=float, default=3e-4)
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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', default=False, action='store_true')
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parser.add_argument('--alpha-lr', type=float, default=3e-4)
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parser.add_argument('--n-step', type=int, default=2)
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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=10000)
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parser.add_argument('--collect-per-step', type=int, default=4)
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parser.add_argument('--update-per-step', type=int, default=1)
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parser.add_argument('--pre-collect-step', type=int, default=10000)
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parser.add_argument('--batch-size', type=int, default=256)
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parser.add_argument('--hidden-sizes', type=int,
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nargs='*', default=[128, 128])
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parser.add_argument('--training-num', type=int, default=16)
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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('--log-interval', type=int, default=1000)
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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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parser.add_argument('--watch', default=False, action='store_true',
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help='watch the play of pre-trained policy only')
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return parser.parse_args()
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def test_sac(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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# 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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# 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 = ActorProb(
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net, args.action_shape, max_action=args.max_action,
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device=args.device, unbounded=True, 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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net_c1 = Net(args.state_shape, args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True, device=args.device)
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critic1 = Critic(net_c1, device=args.device).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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net_c2 = Net(args.state_shape, args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True, device=args.device)
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critic2 = Critic(net_c2, device=args.device).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.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 = SACPolicy(
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actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
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action_range=[env.action_space.low[0], env.action_space.high[0]],
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tau=args.tau, gamma=args.gamma, alpha=args.alpha,
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estimation_step=args.n_step)
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# load a previous policy
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if args.resume_path:
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policy.load_state_dict(torch.load(
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args.resume_path, map_location=args.device
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))
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print("Loaded agent from: ", args.resume_path)
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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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log_path = os.path.join(args.logdir, args.task, 'sac')
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writer = SummaryWriter(log_path)
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def watch():
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# watch agent's performance
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print("Testing agent ...")
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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=[1] * args.test_num,
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render=args.render)
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pprint.pprint(result)
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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 False
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if args.watch:
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watch()
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exit(0)
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# trainer
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train_collector.collect(n_step=args.pre_collect_step, random=True)
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result = offpolicy_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.test_num,
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args.batch_size, args.update_per_step,
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stop_fn=stop_fn, save_fn=save_fn, writer=writer,
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log_interval=args.log_interval)
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pprint.pprint(result)
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watch()
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if __name__ == '__main__':
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test_sac()
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