12d4262f80
A test is not a script and should not be used as such Also marked pistonball test as skipped since it doesn't actually test anything
191 lines
6.9 KiB
Python
191 lines
6.9 KiB
Python
import argparse
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import datetime
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import os
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import pickle
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from test.offline.gather_pendulum_data import expert_file_name, gather_data
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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.exploration import GaussianNoise
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from tianshou.policy import TD3BCPolicy
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from tianshou.policy.base import BasePolicy
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from tianshou.trainer import OfflineTrainer
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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 Actor, Critic
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from tianshou.utils.space_info import SpaceInfo
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def get_args() -> argparse.Namespace:
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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("--hidden-sizes", type=int, nargs="*", default=[64, 64])
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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=5)
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parser.add_argument("--step-per-epoch", type=int, default=500)
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parser.add_argument("--n-step", type=int, default=3)
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parser.add_argument("--batch-size", type=int, default=64)
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parser.add_argument("--alpha", type=float, default=2.5)
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parser.add_argument("--exploration-noise", type=float, default=0.1)
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parser.add_argument("--policy-noise", type=float, default=0.2)
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parser.add_argument("--noise-clip", type=float, default=0.5)
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parser.add_argument("--update-actor-freq", type=int, default=2)
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parser.add_argument("--tau", type=float, default=0.005)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--eval-freq", type=int, default=1)
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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=1 / 35)
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parser.add_argument(
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"--device",
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type=str,
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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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parser.add_argument("--load-buffer-name", type=str, default=expert_file_name())
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return parser.parse_known_args()[0]
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def test_td3_bc(args: argparse.Namespace = get_args()) -> None:
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if os.path.exists(args.load_buffer_name) and os.path.isfile(args.load_buffer_name):
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if args.load_buffer_name.endswith(".hdf5"):
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buffer = VectorReplayBuffer.load_hdf5(args.load_buffer_name)
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else:
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with open(args.load_buffer_name, "rb") as f:
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buffer = pickle.load(f)
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else:
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buffer = gather_data()
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env = gym.make(args.task)
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space_info = SpaceInfo.from_env(env)
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args.state_shape = space_info.observation_info.obs_shape
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args.action_shape = space_info.action_info.action_shape
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args.max_action = space_info.action_info.max_action
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if args.reward_threshold is None:
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# too low?
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default_reward_threshold = {"Pendulum-v0": -1200, "Pendulum-v1": -1200}
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args.reward_threshold = default_reward_threshold.get(
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args.task,
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env.spec.reward_threshold if env.spec else None,
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)
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args.state_dim = space_info.action_info.action_dim
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args.action_dim = space_info.observation_info.obs_dim
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# test_envs = gym.make(args.task)
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test_envs = DummyVectorEnv([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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# actor network
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net_a = Net(
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args.state_shape,
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hidden_sizes=args.hidden_sizes,
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device=args.device,
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)
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actor = Actor(
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net_a,
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action_shape=args.action_shape,
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max_action=args.max_action,
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device=args.device,
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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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# critic network
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net_c1 = Net(
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state_shape=args.state_shape,
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action_shape=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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net_c2 = Net(
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state_shape=args.state_shape,
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action_shape=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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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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policy: TD3BCPolicy = TD3BCPolicy(
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actor=actor,
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actor_optim=actor_optim,
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critic=critic1,
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critic_optim=critic1_optim,
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critic2=critic2,
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critic2_optim=critic2_optim,
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tau=args.tau,
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gamma=args.gamma,
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exploration_noise=GaussianNoise(sigma=args.exploration_noise),
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policy_noise=args.policy_noise,
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update_actor_freq=args.update_actor_freq,
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noise_clip=args.noise_clip,
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alpha=args.alpha,
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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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# load a previous policy
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if args.resume_path:
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policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
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print("Loaded agent from: ", args.resume_path)
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# collector
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# buffer has been gathered
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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("-", "_")}_td3_bc'
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log_path = os.path.join(args.logdir, args.task, "td3_bc", 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 = TensorboardLogger(writer)
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def save_best_fn(policy: BasePolicy) -> None:
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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: float) -> bool:
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return mean_rewards >= args.reward_threshold
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# trainer
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trainer = OfflineTrainer(
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policy=policy,
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buffer=buffer,
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test_collector=test_collector,
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max_epoch=args.epoch,
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step_per_epoch=args.step_per_epoch,
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episode_per_test=args.test_num,
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batch_size=args.batch_size,
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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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for epoch_stat in trainer:
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print(f"Epoch: {epoch_stat.epoch}")
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print(epoch_stat)
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# print(info)
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assert stop_fn(epoch_stat.info_stat.best_reward)
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