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。