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>
181 lines
6.7 KiB
Python
181 lines
6.7 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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import torch.nn as nn
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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 REDQPolicy
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from tianshou.policy.base import BasePolicy
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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 EnsembleLinear, Net
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from tianshou.utils.net.continuous import ActorProb, Critic
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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="Pendulum-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=0)
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parser.add_argument("--buffer-size", type=int, default=20000)
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parser.add_argument("--ensemble-size", type=int, default=4)
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parser.add_argument("--subset-size", type=int, default=2)
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parser.add_argument("--actor-lr", type=float, default=1e-4)
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parser.add_argument("--critic-lr", type=float, default=1e-3)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--tau", type=float, default=0.005)
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parser.add_argument("--alpha", type=float, default=0.2)
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parser.add_argument("--auto-alpha", action="store_true", default=False)
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parser.add_argument("--alpha-lr", type=float, default=3e-4)
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parser.add_argument("--start-timesteps", type=int, default=1000)
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parser.add_argument("--epoch", type=int, default=5)
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parser.add_argument("--step-per-epoch", type=int, default=5000)
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parser.add_argument("--step-per-collect", type=int, default=1)
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parser.add_argument("--update-per-step", type=int, default=3)
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parser.add_argument("--n-step", type=int, default=1)
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parser.add_argument("--batch-size", type=int, default=64)
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parser.add_argument("--target-mode", type=str, choices=("min", "mean"), default="min")
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parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
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parser.add_argument("--training-num", type=int, default=8)
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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(
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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_redq(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.Box, 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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args.max_action = space_info.action_info.max_action
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if args.reward_threshold is None:
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default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250}
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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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# you can also use tianshou.env.SubprocVectorEnv
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# train_envs = gym.make(args.task)
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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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# model
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net = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
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actor = ActorProb(
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net,
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args.action_shape,
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device=args.device,
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unbounded=True,
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conditioned_sigma=True,
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).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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def linear(x: int, y: int) -> nn.Module:
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return EnsembleLinear(args.ensemble_size, x, y)
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net_c = 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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concat=True,
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device=args.device,
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linear_layer=linear,
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)
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critic = Critic(net_c, device=args.device, linear_layer=linear, flatten_input=False).to(
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args.device,
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)
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critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
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action_dim = space_info.action_info.action_dim
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if args.auto_alpha:
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target_entropy = -action_dim
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log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
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alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
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args.alpha = (target_entropy, log_alpha, alpha_optim)
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policy: REDQPolicy = REDQPolicy(
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actor=actor,
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actor_optim=actor_optim,
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critic=critic,
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critic_optim=critic_optim,
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ensemble_size=args.ensemble_size,
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subset_size=args.subset_size,
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tau=args.tau,
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gamma=args.gamma,
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alpha=args.alpha,
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estimation_step=args.n_step,
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actor_delay=args.update_per_step,
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target_mode=args.target_mode,
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action_space=env.action_space,
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)
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# collector
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train_collector = Collector(
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policy,
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train_envs,
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VectorReplayBuffer(args.buffer_size, len(train_envs)),
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exploration_noise=True,
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)
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test_collector = Collector(policy, test_envs)
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train_collector.reset()
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train_collector.collect(n_step=args.start_timesteps, random=True)
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# log
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log_path = os.path.join(args.logdir, args.task, "redq")
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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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# 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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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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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_redq()
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