ca69e79b4a
* Remove flag `eval_mode` from Collector.collect
* Replace flag `is_eval` in BasePolicy with `is_within_training_step` (negating usages)
and set it appropriately in BaseTrainer
220 lines
7.3 KiB
Python
220 lines
7.3 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 gymnasium.spaces import Discrete
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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.offline.utils import load_buffer
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.highlevel.logger import LoggerFactoryDefault
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from tianshou.policy import DiscreteBCQPolicy
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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
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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("--eps-test", type=float, default=0.001)
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parser.add_argument("--lr", type=float, default=6.25e-5)
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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=1)
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parser.add_argument("--target-update-freq", type=int, default=8000)
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parser.add_argument("--unlikely-action-threshold", type=float, default=0.3)
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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=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_bcq(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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assert isinstance(env.action_space, Discrete)
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args.state_shape = env.observation_space.shape
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args.action_shape = int(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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assert args.state_shape is not None
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assert len(args.state_shape) == 3
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c, h, w = args.state_shape
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feature_net = DQN(
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c,
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h,
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w,
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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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policy_net = Actor(
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feature_net,
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args.action_shape,
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device=args.device,
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hidden_sizes=args.hidden_sizes,
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softmax_output=False,
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).to(args.device)
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imitation_net = Actor(
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feature_net,
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args.action_shape,
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device=args.device,
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hidden_sizes=args.hidden_sizes,
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softmax_output=False,
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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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# define policy
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policy: DiscreteBCQPolicy = DiscreteBCQPolicy(
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model=policy_net,
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imitator=imitation_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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estimation_step=args.n_step,
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target_update_freq=args.target_update_freq,
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eval_eps=args.eps_test,
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unlikely_action_threshold=args.unlikely_action_threshold,
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imitation_logits_penalty=args.imitation_logits_penalty,
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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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# 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 = "bcq"
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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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logger_factory = LoggerFactoryDefault()
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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.set_eps(args.eps_test)
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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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result.pprint_asdict()
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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_bcq(get_args())
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