配置一个RL Agent

配置一个RL Agent#

在之前的教程中,我们看到了如何使用 Stable-Baselines3 库训练RL智能体以解决cartpole平衡任务。在本教程中,我们将看到如何配置训练过程以使用不同的RL库和不同的训练算法。

在目录 scripts/reinforcement_learning 中,您将找到不同RL库的脚本。这些按照库名称命名的子目录进行组织。每个子目录包含该库的训练和回放脚本。

要为特定任务配置学习库,您需要为学习代理创建一个配置文件。这个配置文件用于创建学习代理的实例,并用于配置训练过程。类似于在:ref:`tutorial-register-rl-env-gym`教程中显示的环境注册,您可以使用``gymnasium.register``方法注册学习代理。

代码#

作为示例,我们将查看``isaaclab_tasks``包中为任务``Isaac-Cartpole-v0``包含的配置。这是我们在:ref:`tutorial-run-rl-training`教程中使用的相同任务。

gym.register(
    id="Isaac-Cartpole-v0",
    entry_point="isaaclab.envs:ManagerBasedRLEnv",
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:CartpoleEnvCfg",
        "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml",
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CartpolePPORunnerCfg",
        "rsl_rl_with_symmetry_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CartpolePPORunnerWithSymmetryCfg",
        "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml",
        "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml",
    },

代码详解#

在属性`kwargs`下,我们可以看到不同学习库的配置。 键是库的名称,值是配置实例的路径。 这个配置实例可以是字符串、类或类的实例。 例如,键`”rl_games_cfg_entry_point”的值是指向RL-Games库配置YAML文件的字符串。 同时,键”rsl_rl_cfg_entry_point”`的值指向RSL-RL库的配置类。

用于指定代理配置类的模式与用于指定环境配置入口点的模式非常相似。这意味着以下内容是等效的:

指定配置入口点为字符串
from . import agents

gym.register(
   id="Isaac-Cartpole-v0",
   entry_point="isaaclab.envs:ManagerBasedRLEnv",
   disable_env_checker=True,
   kwargs={
      "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:CartpoleEnvCfg",
      "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CartpolePPORunnerCfg",
   },
)
指定配置入口点为一个类
from . import agents

gym.register(
   id="Isaac-Cartpole-v0",
   entry_point="isaaclab.envs:ManagerBasedRLEnv",
   disable_env_checker=True,
   kwargs={
      "env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:CartpoleEnvCfg",
      "rsl_rl_cfg_entry_point": agents.rsl_rl_ppo_cfg.CartpolePPORunnerCfg,
   },
)

第一个代码块是指定配置入口的首选方式。第二个代码块等同于第一个代码块,但会导致配置类的导入,从而减慢导入时间。这就是为什么我们建议使用字符串作为配置入口的原因。

所有位于 scripts/reinforcement_learning 目录中的脚本都默认配置为从 kwargs 字典中读取 <library_name>_cfg_entry_point 以获取配置实例。

例如,以下代码块显示了 train.py 脚本如何读取 Stable-Baselines3 库的配置实例:

使用SB3的train.py代码
  1# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
  2# All rights reserved.
  3#
  4# SPDX-License-Identifier: BSD-3-Clause
  5
  6
  7"""Script to train RL agent with Stable Baselines3."""
  8
  9"""Launch Isaac Sim Simulator first."""
 10
 11import argparse
 12import contextlib
 13import signal
 14import sys
 15from pathlib import Path
 16
 17from isaaclab.app import AppLauncher
 18
 19# add argparse arguments
 20parser = argparse.ArgumentParser(description="Train an RL agent with Stable-Baselines3.")
 21parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
 22parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
 23parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).")
 24parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
 25parser.add_argument("--task", type=str, default=None, help="Name of the task.")
 26parser.add_argument(
 27    "--agent", type=str, default="sb3_cfg_entry_point", help="Name of the RL agent configuration entry point."
 28)
 29parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
 30parser.add_argument("--log_interval", type=int, default=100_000, help="Log data every n timesteps.")
 31parser.add_argument("--checkpoint", type=str, default=None, help="Continue the training from checkpoint.")
 32parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
 33parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.")
 34parser.add_argument(
 35    "--keep_all_info",
 36    action="store_true",
 37    default=False,
 38    help="Use a slower SB3 wrapper but keep all the extra training info.",
 39)
 40# append AppLauncher cli args
 41AppLauncher.add_app_launcher_args(parser)
 42# parse the arguments
 43args_cli, hydra_args = parser.parse_known_args()
 44# always enable cameras to record video
 45if args_cli.video:
 46    args_cli.enable_cameras = True
 47
 48# clear out sys.argv for Hydra
 49sys.argv = [sys.argv[0]] + hydra_args
 50
 51# launch omniverse app
 52app_launcher = AppLauncher(args_cli)
 53simulation_app = app_launcher.app
 54
 55
 56def cleanup_pbar(*args):
 57    """
 58    A small helper to stop training and
 59    cleanup progress bar properly on ctrl+c
 60    """
 61    import gc
 62
 63    tqdm_objects = [obj for obj in gc.get_objects() if "tqdm" in type(obj).__name__]
 64    for tqdm_object in tqdm_objects:
 65        if "tqdm_rich" in type(tqdm_object).__name__:
 66            tqdm_object.close()
 67    raise KeyboardInterrupt
 68
 69
 70# disable KeyboardInterrupt override
 71signal.signal(signal.SIGINT, cleanup_pbar)
 72
 73"""Rest everything follows."""
 74
 75import gymnasium as gym
 76import numpy as np
 77import os
 78import random
 79from datetime import datetime
 80
 81import omni
 82from stable_baselines3 import PPO
 83from stable_baselines3.common.callbacks import CheckpointCallback, LogEveryNTimesteps
 84from stable_baselines3.common.vec_env import VecNormalize
 85
 86from isaaclab.envs import (
 87    DirectMARLEnv,
 88    DirectMARLEnvCfg,
 89    DirectRLEnvCfg,
 90    ManagerBasedRLEnvCfg,
 91    multi_agent_to_single_agent,
 92)
 93from isaaclab.utils.dict import print_dict
 94from isaaclab.utils.io import dump_pickle, dump_yaml
 95
 96from isaaclab_rl.sb3 import Sb3VecEnvWrapper, process_sb3_cfg
 97
 98import isaaclab_tasks  # noqa: F401
 99from isaaclab_tasks.utils.hydra import hydra_task_config
100
101# PLACEHOLDER: Extension template (do not remove this comment)
102
103
104@hydra_task_config(args_cli.task, args_cli.agent)
105def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict):
106    """Train with stable-baselines agent."""
107    # randomly sample a seed if seed = -1
108    if args_cli.seed == -1:
109        args_cli.seed = random.randint(0, 10000)
110
111    # override configurations with non-hydra CLI arguments
112    env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
113    agent_cfg["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"]
114    # max iterations for training
115    if args_cli.max_iterations is not None:
116        agent_cfg["n_timesteps"] = args_cli.max_iterations * agent_cfg["n_steps"] * env_cfg.scene.num_envs
117
118    # set the environment seed
119    # note: certain randomizations occur in the environment initialization so we set the seed here
120    env_cfg.seed = agent_cfg["seed"]
121    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
122
123    # directory for logging into
124    run_info = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
125    log_root_path = os.path.abspath(os.path.join("logs", "sb3", args_cli.task))
126    print(f"[INFO] Logging experiment in directory: {log_root_path}")
127    # The Ray Tune workflow extracts experiment name using the logging line below, hence, do not change it (see PR #2346, comment-2819298849)
128    print(f"Exact experiment name requested from command line: {run_info}")
129    log_dir = os.path.join(log_root_path, run_info)
130    # dump the configuration into log-directory
131    dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg)
132    dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg)
133    dump_pickle(os.path.join(log_dir, "params", "env.pkl"), env_cfg)
134    dump_pickle(os.path.join(log_dir, "params", "agent.pkl"), agent_cfg)
135
136    # save command used to run the script
137    command = " ".join(sys.orig_argv)
138    (Path(log_dir) / "command.txt").write_text(command)
139
140    # post-process agent configuration
141    agent_cfg = process_sb3_cfg(agent_cfg, env_cfg.scene.num_envs)
142    # read configurations about the agent-training
143    policy_arch = agent_cfg.pop("policy")
144    n_timesteps = agent_cfg.pop("n_timesteps")
145
146    # set the IO descriptors output directory if requested
147    if isinstance(env_cfg, ManagerBasedRLEnvCfg):
148        env_cfg.export_io_descriptors = args_cli.export_io_descriptors
149        env_cfg.io_descriptors_output_dir = log_dir
150    else:
151        omni.log.warn(
152            "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported."
153        )
154
155    # create isaac environment
156    env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
157
158    # convert to single-agent instance if required by the RL algorithm
159    if isinstance(env.unwrapped, DirectMARLEnv):
160        env = multi_agent_to_single_agent(env)
161
162    # wrap for video recording
163    if args_cli.video:
164        video_kwargs = {
165            "video_folder": os.path.join(log_dir, "videos", "train"),
166            "step_trigger": lambda step: step % args_cli.video_interval == 0,
167            "video_length": args_cli.video_length,
168            "disable_logger": True,
169        }
170        print("[INFO] Recording videos during training.")
171        print_dict(video_kwargs, nesting=4)
172        env = gym.wrappers.RecordVideo(env, **video_kwargs)
173
174    # wrap around environment for stable baselines
175    env = Sb3VecEnvWrapper(env, fast_variant=not args_cli.keep_all_info)
176
177    norm_keys = {"normalize_input", "normalize_value", "clip_obs"}
178    norm_args = {}
179    for key in norm_keys:
180        if key in agent_cfg:
181            norm_args[key] = agent_cfg.pop(key)
182
183    if norm_args and norm_args.get("normalize_input"):
184        print(f"Normalizing input, {norm_args=}")
185        env = VecNormalize(
186            env,
187            training=True,
188            norm_obs=norm_args["normalize_input"],
189            norm_reward=norm_args.get("normalize_value", False),
190            clip_obs=norm_args.get("clip_obs", 100.0),
191            gamma=agent_cfg["gamma"],
192            clip_reward=np.inf,
193        )
194
195    # create agent from stable baselines
196    agent = PPO(policy_arch, env, verbose=1, tensorboard_log=log_dir, **agent_cfg)
197    if args_cli.checkpoint is not None:
198        agent = agent.load(args_cli.checkpoint, env, print_system_info=True)
199
200    # callbacks for agent
201    checkpoint_callback = CheckpointCallback(save_freq=1000, save_path=log_dir, name_prefix="model", verbose=2)
202    callbacks = [checkpoint_callback, LogEveryNTimesteps(n_steps=args_cli.log_interval)]
203
204    # train the agent
205    with contextlib.suppress(KeyboardInterrupt):
206        agent.learn(
207            total_timesteps=n_timesteps,
208            callback=callbacks,
209            progress_bar=True,
210            log_interval=None,
211        )
212    # save the final model
213    agent.save(os.path.join(log_dir, "model"))
214    print("Saving to:")
215    print(os.path.join(log_dir, "model.zip"))
216
217    if isinstance(env, VecNormalize):
218        print("Saving normalization")
219        env.save(os.path.join(log_dir, "model_vecnormalize.pkl"))
220
221    # close the simulator
222    env.close()
223
224
225if __name__ == "__main__":
226    # run the main function
227    main()
228    # close sim app
229    simulation_app.close()

参数 --agent 用于指定要使用的学习库。这用于从 kwargs 字典中检索配置实例。您可以通过传递 --agent 参数来手动指定替代配置实例。

代码执行#

由于cartpole平衡任务,RSL-RL 库提供两个配置实例,我们可以使用 --agent 参数来指定要使用的配置实例。

  • 使用标准PPO配置进行训练:

    # standard PPO training
    ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --headless \
      --run_name ppo
    
  • 使用具有对称增强的PPO配置进行训练:

    # PPO training with symmetry augmentation
    ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --headless \
      --agent rsl_rl_with_symmetry_cfg_entry_point \
      --run_name ppo_with_symmetry_data_augmentation
    
    # you can use hydra to disable symmetry augmentation but enable mirror loss computation
    ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --headless \
      --agent rsl_rl_with_symmetry_cfg_entry_point \
      --run_name ppo_without_symmetry_data_augmentation \
      agent.algorithm.symmetry_cfg.use_data_augmentation=false
    

--run_name 参数用于指定运行的名称。这用于在``logs/rsl_rl/cartpole``目录中为运行创建一个目录。