4f65b131aa
Closes: #1058 ### Api Extensions - Batch received two new methods: `to_dict` and `to_list_of_dicts`. #1063 - `Collector`s can now be closed, and their reset is more granular. #1063 - Trainers can control whether collectors should be reset prior to training. #1063 - Convenience constructor for `CollectStats` called `with_autogenerated_stats`. #1063 ### Internal Improvements - `Collector`s rely less on state, the few stateful things are stored explicitly instead of through a `.data` attribute. #1063 - Introduced a first iteration of a naming convention for vars in `Collector`s. #1063 - Generally improved readability of Collector code and associated tests (still quite some way to go). #1063 - Improved typing for `exploration_noise` and within Collector. #1063 ### Breaking Changes - Removed `.data` attribute from `Collector` and its child classes. #1063 - Collectors no longer reset the environment on initialization. Instead, the user might have to call `reset` expicitly or pass `reset_before_collect=True` . #1063 - VectorEnvs now return an array of info-dicts on reset instead of a list. #1063 - Fixed `iter(Batch(...)` which now behaves the same way as `Batch(...).__iter__()`. Can be considered a bugfix. #1063 --------- Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
214 lines
7.7 KiB
Python
214 lines
7.7 KiB
Python
import argparse
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import os
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import pprint
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from typing import cast
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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, PrioritizedVectorReplayBuffer, VectorReplayBuffer
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from tianshou.env import DummyVectorEnv
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from tianshou.policy import DQNPolicy, ICMPolicy
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from tianshou.policy.base import BasePolicy
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from tianshou.policy.modelfree.dqn import DQNTrainingStats
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from tianshou.trainer import OffpolicyTrainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import MLP, Net
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from tianshou.utils.net.discrete import IntrinsicCuriosityModule
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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-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=0.05)
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parser.add_argument("--eps-train", type=float, default=0.1)
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parser.add_argument("--buffer-size", type=int, default=20000)
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parser.add_argument("--lr", type=float, default=1e-3)
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parser.add_argument("--gamma", type=float, default=0.9)
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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("--epoch", type=int, default=20)
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parser.add_argument("--step-per-epoch", type=int, default=10000)
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parser.add_argument("--step-per-collect", type=int, default=10)
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parser.add_argument("--update-per-step", type=float, default=0.1)
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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=[128, 128, 128, 128])
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parser.add_argument("--training-num", type=int, default=10)
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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("--prioritized-replay", action="store_true", default=False)
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parser.add_argument("--alpha", type=float, default=0.6)
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parser.add_argument("--beta", type=float, default=0.4)
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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(
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"--lr-scale",
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type=float,
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default=1.0,
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help="use intrinsic curiosity module with this lr scale",
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)
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parser.add_argument(
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"--reward-scale",
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type=float,
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default=0.01,
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help="scaling factor for intrinsic curiosity reward",
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)
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parser.add_argument(
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"--forward-loss-weight",
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type=float,
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default=0.2,
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help="weight for the forward model loss in ICM",
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)
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return parser.parse_known_args()[0]
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def test_dqn_icm(args: argparse.Namespace = get_args()) -> None:
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env = gym.make(args.task)
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env.action_space = cast(gym.spaces.Discrete, env.action_space)
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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-v0": 195}
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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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# train_envs = gym.make(args.task)
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# you can also use tianshou.env.SubprocVectorEnv
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train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)])
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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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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# Q_param = V_param = {"hidden_sizes": [128]}
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# model
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net = Net(
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args.state_shape,
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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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# dueling=(Q_param, V_param),
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).to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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policy: DQNPolicy[DQNTrainingStats] = DQNPolicy(
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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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estimation_step=args.n_step,
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target_update_freq=args.target_update_freq,
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)
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feature_dim = args.hidden_sizes[-1]
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obs_dim = space_info.observation_info.obs_dim
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feature_net = MLP(
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obs_dim,
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output_dim=feature_dim,
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hidden_sizes=args.hidden_sizes[:-1],
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device=args.device,
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)
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action_dim = space_info.action_info.action_dim
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icm_net = IntrinsicCuriosityModule(
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feature_net,
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feature_dim,
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action_dim,
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hidden_sizes=args.hidden_sizes[-1:],
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device=args.device,
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).to(args.device)
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icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr)
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policy: ICMPolicy = ICMPolicy(
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policy=policy,
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model=icm_net,
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optim=icm_optim,
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action_space=env.action_space,
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lr_scale=args.lr_scale,
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reward_scale=args.reward_scale,
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forward_loss_weight=args.forward_loss_weight,
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)
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# buffer
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buf: PrioritizedVectorReplayBuffer | VectorReplayBuffer
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if args.prioritized_replay:
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buf = PrioritizedVectorReplayBuffer(
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args.buffer_size,
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buffer_num=len(train_envs),
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alpha=args.alpha,
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beta=args.beta,
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)
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else:
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buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs))
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# collector
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train_collector = Collector(policy, train_envs, buf, exploration_noise=True)
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test_collector = Collector(policy, test_envs, exploration_noise=True)
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# policy.set_eps(1)
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train_collector.collect(n_step=args.batch_size * args.training_num, reset_before_collect=True)
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# log
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log_path = os.path.join(args.logdir, args.task, "dqn_icm")
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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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def train_fn(epoch: int, env_step: int) -> None:
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# eps annnealing, just a demo
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if env_step <= 10000:
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policy.set_eps(args.eps_train)
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elif env_step <= 50000:
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eps = args.eps_train - (env_step - 10000) / 40000 * (0.9 * args.eps_train)
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policy.set_eps(eps)
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else:
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policy.set_eps(0.1 * args.eps_train)
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def test_fn(epoch: int, env_step: int | None) -> None:
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policy.set_eps(args.eps_test)
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# trainer
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result = OffpolicyTrainer(
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policy=policy,
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train_collector=train_collector,
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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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step_per_collect=args.step_per_collect,
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episode_per_test=args.test_num,
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batch_size=args.batch_size,
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update_per_step=args.update_per_step,
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train_fn=train_fn,
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test_fn=test_fn,
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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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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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collector_stats = collector.collect(n_episode=1, render=args.render)
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print(collector_stats)
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if __name__ == "__main__":
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test_dqn_icm(get_args())
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