5ecea2402e
- Fix save_checkpoint_fn return value to checkpoint_path; - Fix wrong link in doc; - Fix an off-by-one bug in trainer iterator.
177 lines
6.3 KiB
Python
177 lines
6.3 KiB
Python
import argparse
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import os
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import pickle
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import pprint
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import 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 DiscreteBCQPolicy
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from tianshou.trainer import offline_trainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import ActorCritic, Net
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from tianshou.utils.net.discrete import Actor
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if __name__ == "__main__":
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from gather_cartpole_data import expert_file_name, gather_data
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else: # pytest
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from test.offline.gather_cartpole_data import expert_file_name, gather_data
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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="CartPole-v0")
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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-4)
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parser.add_argument("--gamma", type=float, default=0.99)
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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=320)
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parser.add_argument("--unlikely-action-threshold", type=float, default=0.6)
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parser.add_argument("--imitation-logits-penalty", type=float, default=0.01)
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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=2000)
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parser.add_argument("--batch-size", type=int, default=64)
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parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 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.)
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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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parser.add_argument("--resume", action="store_true")
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parser.add_argument("--save-interval", type=int, default=4)
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args = parser.parse_known_args()[0]
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return args
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def test_discrete_bcq(args=get_args()):
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# envs
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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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if args.reward_threshold is None:
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default_reward_threshold = {"CartPole-v0": 190}
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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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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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test_envs.seed(args.seed)
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# model
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net = Net(args.state_shape, args.hidden_sizes[0], device=args.device)
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policy_net = Actor(
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net, args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device
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).to(args.device)
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imitation_net = Actor(
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net, args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device
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).to(args.device)
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actor_critic = ActorCritic(policy_net, imitation_net)
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optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
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policy = DiscreteBCQPolicy(
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policy_net,
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imitation_net,
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optim,
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args.gamma,
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args.n_step,
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args.target_update_freq,
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args.eps_test,
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args.unlikely_action_threshold,
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args.imitation_logits_penalty,
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)
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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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buffer = pickle.load(open(args.load_buffer_name, "rb"))
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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_bcq")
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writer = SummaryWriter(log_path)
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logger = TensorboardLogger(writer, save_interval=args.save_interval)
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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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def save_checkpoint_fn(epoch, env_step, gradient_step):
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# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
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ckpt_path = os.path.join(log_path, "checkpoint.pth")
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# Example: saving by epoch num
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# ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
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torch.save(
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{
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"model": policy.state_dict(),
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"optim": optim.state_dict(),
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}, ckpt_path
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)
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return ckpt_path
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if args.resume:
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# load from existing checkpoint
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print(f"Loading agent under {log_path}")
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ckpt_path = os.path.join(log_path, "checkpoint.pth")
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if os.path.exists(ckpt_path):
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checkpoint = torch.load(ckpt_path, map_location=args.device)
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policy.load_state_dict(checkpoint["model"])
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optim.load_state_dict(checkpoint["optim"])
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print("Successfully restore policy and optim.")
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else:
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print("Fail to restore policy and optim.")
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result = offline_trainer(
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policy,
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buffer,
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test_collector,
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args.epoch,
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args.update_per_epoch,
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args.test_num,
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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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resume_from_log=args.resume,
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save_checkpoint_fn=save_checkpoint_fn,
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)
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assert stop_fn(result["best_reward"])
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if __name__ == "__main__":
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pprint.pprint(result)
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# Let's watch its performance!
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env = gym.make(args.task)
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policy.eval()
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policy.set_eps(args.eps_test)
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collector = Collector(policy, env)
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result = collector.collect(n_episode=1, render=args.render)
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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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def test_discrete_bcq_resume(args=get_args()):
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args.resume = True
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test_discrete_bcq(args)
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if __name__ == "__main__":
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test_discrete_bcq(get_args())
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