eb0215cf76
Improves typing in examples and tests, towards mypy passing there. Introduces the SpaceInfo utility
213 lines
7.2 KiB
Python
213 lines
7.2 KiB
Python
#!/usr/bin/env python3
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import argparse
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import datetime
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import os
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import pickle
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import pprint
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import sys
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import numpy as np
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import torch
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from examples.atari.atari_network import DQN
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from examples.atari.atari_wrapper import make_atari_env
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from examples.common import logger_factory
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from examples.offline.utils import load_buffer
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.policy import DiscreteCRRPolicy
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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.net.common import ActorCritic
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from tianshou.utils.net.discrete import Actor, Critic
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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="PongNoFrameskip-v4")
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parser.add_argument("--seed", type=int, default=1626)
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parser.add_argument("--lr", type=float, default=0.0001)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--policy-improvement-mode", type=str, default="exp")
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parser.add_argument("--ratio-upper-bound", type=float, default=20.0)
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parser.add_argument("--beta", type=float, default=1.0)
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parser.add_argument("--min-q-weight", type=float, default=10.0)
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parser.add_argument("--target-update-freq", type=int, default=500)
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parser.add_argument("--epoch", type=int, default=100)
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parser.add_argument("--update-per-epoch", type=int, default=10000)
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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=[512])
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parser.add_argument("--test-num", type=int, default=10)
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parser.add_argument("--frames-stack", type=int, default=4)
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parser.add_argument("--scale-obs", type=int, default=0)
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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("--resume-path", type=str, default=None)
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parser.add_argument("--resume-id", type=str, default=None)
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parser.add_argument(
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"--logger",
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type=str,
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default="tensorboard",
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choices=["tensorboard", "wandb"],
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)
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parser.add_argument("--wandb-project", type=str, default="offline_atari.benchmark")
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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("--log-interval", type=int, default=100)
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parser.add_argument(
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"--load-buffer-name",
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type=str,
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default="./expert_DQN_PongNoFrameskip-v4.hdf5",
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)
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parser.add_argument("--buffer-from-rl-unplugged", action="store_true", default=False)
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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_crr(args: argparse.Namespace = get_args()) -> None:
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# envs
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env, _, test_envs = make_atari_env(
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args.task,
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args.seed,
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1,
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args.test_num,
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scale=args.scale_obs,
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frame_stack=args.frames_stack,
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)
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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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# should be N_FRAMES x H x W
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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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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# model
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feature_net = DQN(
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*args.state_shape,
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args.action_shape,
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device=args.device,
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features_only=True,
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).to(args.device)
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actor = Actor(
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feature_net,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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device=args.device,
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softmax_output=False,
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).to(args.device)
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critic = Critic(
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feature_net,
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hidden_sizes=args.hidden_sizes,
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last_size=np.prod(args.action_shape),
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device=args.device,
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).to(args.device)
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actor_critic = ActorCritic(actor, critic)
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optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
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# define policy
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policy: DiscreteCRRPolicy = DiscreteCRRPolicy(
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actor=actor,
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critic=critic,
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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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policy_improvement_mode=args.policy_improvement_mode,
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ratio_upper_bound=args.ratio_upper_bound,
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beta=args.beta,
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min_q_weight=args.min_q_weight,
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target_update_freq=args.target_update_freq,
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).to(args.device)
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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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# buffer
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if args.buffer_from_rl_unplugged:
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buffer = load_buffer(args.load_buffer_name)
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else:
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assert os.path.exists(
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args.load_buffer_name,
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), "Please run atari_dqn.py first to get expert's data buffer."
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if args.load_buffer_name.endswith(".pkl"):
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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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elif 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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print(f"Unknown buffer format: {args.load_buffer_name}")
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sys.exit(0)
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print("Replay buffer size:", len(buffer), flush=True)
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# collector
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test_collector = Collector(policy, test_envs, exploration_noise=True)
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# log
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now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
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args.algo_name = "crr"
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log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
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log_path = os.path.join(args.logdir, log_name)
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# logger
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if args.logger == "wandb":
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logger_factory.logger_type = "wandb"
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logger_factory.wandb_project = args.wandb_project
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else:
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logger_factory.logger_type = "tensorboard"
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logger = logger_factory.create_logger(
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log_dir=log_path,
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experiment_name=log_name,
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run_id=args.resume_id,
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config_dict=vars(args),
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)
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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 False
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# watch agent's performance
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def watch() -> None:
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print("Setup test envs ...")
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policy.eval()
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test_envs.seed(args.seed)
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print("Testing agent ...")
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test_collector.reset()
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result = test_collector.collect(n_episode=args.test_num, render=args.render)
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pprint.pprint(result)
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rew = result.returns_stat.mean
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print(f"Mean reward (over {result.n_collected_episodes} episodes): {rew}")
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if args.watch:
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watch()
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sys.exit(0)
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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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pprint.pprint(result)
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watch()
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if __name__ == "__main__":
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test_discrete_crr(get_args())
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