1423eeb3b2
- Fix the current bug discussed in #844 in `test_ppo.py`. - Add warning for `ActorProb ` if both `max_action ` and `unbounded=True` are used for model initializations. - Add warning for PGpolicy and DDPGpolicy if they find duplicate usage of action-bounded actor and action scaling method.
179 lines
6.4 KiB
Python
179 lines
6.4 KiB
Python
import argparse
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import os
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import pickle
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import gymnasium as 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 SACPolicy
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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 Net
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from tianshou.utils.net.continuous import ActorProb, Critic
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def expert_file_name():
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return os.path.join(os.path.dirname(__file__), "expert_SAC_Pendulum-v1.pkl")
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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('--hidden-sizes', type=int, nargs='*', default=[128, 128])
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parser.add_argument('--actor-lr', type=float, default=1e-3)
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parser.add_argument('--critic-lr', type=float, default=1e-3)
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parser.add_argument('--epoch', type=int, default=7)
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parser.add_argument('--step-per-epoch', type=int, default=8000)
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parser.add_argument('--batch-size', type=int, default=256)
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parser.add_argument('--training-num', type=int, default=10)
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parser.add_argument('--test-num', type=int, default=10)
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parser.add_argument('--step-per-collect', type=int, default=10)
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parser.add_argument('--update-per-step', type=float, default=0.125)
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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("--gamma", default=0.99)
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parser.add_argument("--tau", default=0.005)
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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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parser.add_argument('--resume-path', type=str, default=None)
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parser.add_argument(
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'--watch',
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default=False,
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action='store_true',
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help='watch the play of pre-trained policy only'
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)
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# sac:
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parser.add_argument('--alpha', type=float, default=0.2)
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parser.add_argument('--auto-alpha', type=int, default=1)
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parser.add_argument('--alpha-lr', type=float, default=3e-4)
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parser.add_argument('--rew-norm', action="store_true", default=False)
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parser.add_argument('--n-step', type=int, default=3)
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parser.add_argument("--save-buffer-name", type=str, default=expert_file_name())
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args = parser.parse_known_args()[0]
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return args
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def gather_data():
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"""Return expert buffer data."""
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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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device=args.device,
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unbounded=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(
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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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)
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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(
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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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)
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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,
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actor_optim,
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critic1,
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critic1_optim,
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critic2,
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critic2_optim,
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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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reward_normalization=args.rew_norm,
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estimation_step=args.n_step,
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action_space=env.action_space,
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)
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# collector
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buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
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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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# train_collector.collect(n_step=args.buffer_size)
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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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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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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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save_best_fn=save_best_fn,
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stop_fn=stop_fn,
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logger=logger,
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)
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train_collector.reset()
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result = train_collector.collect(n_step=args.buffer_size)
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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 args.save_buffer_name.endswith(".hdf5"):
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buffer.save_hdf5(args.save_buffer_name)
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else:
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pickle.dump(buffer, open(args.save_buffer_name, "wb"))
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return buffer
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