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
124 lines
4.6 KiB
Python
124 lines
4.6 KiB
Python
import argparse
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import os
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import pickle
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from test.offline.gather_cartpole_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.policy import BasePolicy, DiscreteCQLPolicy
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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.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="CartPole-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=1626)
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parser.add_argument("--eps-test", type=float, default=0.001)
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parser.add_argument("--lr", type=float, default=3e-3)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--num-quantiles", type=int, default=200)
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parser.add_argument("--n-step", type=int, default=3)
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parser.add_argument("--target-update-freq", type=int, default=500)
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parser.add_argument("--min-q-weight", type=float, default=10.0)
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parser.add_argument("--epoch", type=int, default=5)
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parser.add_argument("--update-per-epoch", type=int, default=1000)
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parser.add_argument("--batch-size", type=int, default=32)
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parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64])
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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=0.0)
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parser.add_argument("--load-buffer-name", type=str, default=expert_file_name())
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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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return parser.parse_known_args()[0]
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def test_discrete_cql(args: argparse.Namespace = get_args()) -> None:
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# envs
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env = gym.make(args.task)
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assert isinstance(env.action_space, gym.spaces.Discrete)
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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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if args.reward_threshold is None:
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default_reward_threshold = {"CartPole-v1": 170}
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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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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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net = 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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device=args.device,
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softmax=False,
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num_atoms=args.num_quantiles,
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)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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policy: DiscreteCQLPolicy = DiscreteCQLPolicy(
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model=net,
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optim=optim,
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action_space=env.action_space,
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discount_factor=args.gamma,
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num_quantiles=args.num_quantiles,
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estimation_step=args.n_step,
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target_update_freq=args.target_update_freq,
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min_q_weight=args.min_q_weight,
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).to(args.device)
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# buffer
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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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# collector
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test_collector = Collector(policy, test_envs, exploration_noise=True)
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log_path = os.path.join(args.logdir, args.task, "discrete_cql")
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writer = SummaryWriter(log_path)
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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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result = 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.update_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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stop_fn=stop_fn,
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save_best_fn=save_best_fn,
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logger=logger,
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).run()
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assert stop_fn(result.best_reward)
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