Configuring an RL Agent

In the previous tutorial, we saw how to train an RL agent to solve the cartpole balancing task using the Stable-Baselines3 library. In this tutorial, we will see how to configure the training process to use different RL libraries and different training algorithms.

In the directory scripts/reinforcement_learning, you will find the scripts for different RL libraries. These are organized into subdirectories named after the library name. Each subdirectory contains the training and playing scripts for the library.

To configure a learning library with a specific task, you need to create a configuration file for the learning agent. This configuration file is used to create an instance of the learning agent and is used to configure the training process. Similar to the environment registration shown in the Registering an Environment tutorial, you can register the learning agent with the gymnasium.register method.

The Code

As an example, we will look at the configuration included for the task Isaac-Cartpole-v0 in the isaaclab_tasks package. This is the same task that we used in the Training with an RL Agent tutorial.

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",
    },

The Code Explained

Under the attribute kwargs, we can see the configuration for the different learning libraries. The key is the name of the library and the value is the path to the configuration instance. This configuration instance can be a string, a class, or an instance of the class. For example, the value of the key "rl_games_cfg_entry_point" is a string that points to the configuration YAML file for the RL-Games library. Meanwhile, the value of the key "rsl_rl_cfg_entry_point" points to the configuration class for the RSL-RL library.

The pattern used for specifying an agent configuration class follows closely to that used for specifying the environment configuration entry point. This means that while the following are equivalent:

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",
   },
)

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,
   },
)

The first code block is the preferred way to specify the configuration entry point. The second code block is equivalent to the first one, but it leads to import of the configuration class which slows down the import time. This is why we recommend using strings for the configuration entry point.

All the scripts in the scripts/reinforcement_learning directory are configured by default to read the <library_name>_cfg_entry_point from the kwargs dictionary to retrieve the configuration instance.

For instance, the following code block shows how the train.py script reads the configuration instance for the Stable-Baselines3 library:

# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Script to train RL agent with Stable Baselines3."""
"""Launch Isaac Sim Simulator first."""
import argparse
import contextlib
import signal
import sys
from pathlib import Path
from isaaclab.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(description="Train an RL agent with Stable-Baselines3.")
parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).")
parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default=None, help="Name of the task.")
parser.add_argument(
    "--agent", type=str, default="sb3_cfg_entry_point", help="Name of the RL agent configuration entry point."
)
parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
parser.add_argument("--log_interval", type=int, default=100_000, help="Log data every n timesteps.")
parser.add_argument("--checkpoint", type=str, default=None, help="Continue the training from checkpoint.")
parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.")
parser.add_argument(
    "--keep_all_info",
    action="store_true",
    default=False,
    help="Use a slower SB3 wrapper but keep all the extra training info.",
)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli, hydra_args = parser.parse_known_args()
# always enable cameras to record video
if args_cli.video:
    args_cli.enable_cameras = True
# clear out sys.argv for Hydra
sys.argv = [sys.argv[0]] + hydra_args
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
def cleanup_pbar(*args):
    """
    A small helper to stop training and
    cleanup progress bar properly on ctrl+c
    """
    import gc
    tqdm_objects = [obj for obj in gc.get_objects() if "tqdm" in type(obj).__name__]
    for tqdm_object in tqdm_objects:
        if "tqdm_rich" in type(tqdm_object).__name__:
            tqdm_object.close()
    raise KeyboardInterrupt
# disable KeyboardInterrupt override
signal.signal(signal.SIGINT, cleanup_pbar)
"""Rest everything follows."""
import gymnasium as gym
import numpy as np
import os
import random
from datetime import datetime
import omni
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import CheckpointCallback, LogEveryNTimesteps
from stable_baselines3.common.vec_env import VecNormalize
from isaaclab.envs import (
    DirectMARLEnv,
    DirectMARLEnvCfg,
    DirectRLEnvCfg,
    ManagerBasedRLEnvCfg,
    multi_agent_to_single_agent,
)
from isaaclab.utils.dict import print_dict
from isaaclab.utils.io import dump_yaml
from isaaclab_rl.sb3 import Sb3VecEnvWrapper, process_sb3_cfg
import isaaclab_tasks  # noqa: F401
from isaaclab_tasks.utils.hydra import hydra_task_config
# PLACEHOLDER: Extension template (do not remove this comment)
@hydra_task_config(args_cli.task, args_cli.agent)
def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict):
    """Train with stable-baselines agent."""
    # randomly sample a seed if seed = -1
    if args_cli.seed == -1:
        args_cli.seed = random.randint(0, 10000)
    # override configurations with non-hydra CLI arguments
    env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
    agent_cfg["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"]
    # max iterations for training
    if args_cli.max_iterations is not None:
        agent_cfg["n_timesteps"] = args_cli.max_iterations * agent_cfg["n_steps"] * env_cfg.scene.num_envs
    # set the environment seed
    # note: certain randomizations occur in the environment initialization so we set the seed here
    env_cfg.seed = agent_cfg["seed"]
    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
    # directory for logging into
    run_info = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    log_root_path = os.path.abspath(os.path.join("logs", "sb3", args_cli.task))
    print(f"[INFO] Logging experiment in directory: {log_root_path}")
    # The Ray Tune workflow extracts experiment name using the logging line below, hence, do not change it (see PR #2346, comment-2819298849)
    print(f"Exact experiment name requested from command line: {run_info}")
    log_dir = os.path.join(log_root_path, run_info)
    # dump the configuration into log-directory
    dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg)
    dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg)
    # save command used to run the script
    command = " ".join(sys.orig_argv)
    (Path(log_dir) / "command.txt").write_text(command)
    # post-process agent configuration
    agent_cfg = process_sb3_cfg(agent_cfg, env_cfg.scene.num_envs)
    # read configurations about the agent-training
    policy_arch = agent_cfg.pop("policy")
    n_timesteps = agent_cfg.pop("n_timesteps")
    # set the IO descriptors output directory if requested
    if isinstance(env_cfg, ManagerBasedRLEnvCfg):
        env_cfg.export_io_descriptors = args_cli.export_io_descriptors
        env_cfg.io_descriptors_output_dir = log_dir
    else:
        omni.log.warn(
            "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported."
        )
    # set the log directory for the environment (works for all environment types)
    env_cfg.log_dir = log_dir
    # create isaac environment
    env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
    # convert to single-agent instance if required by the RL algorithm
    if isinstance(env.unwrapped, DirectMARLEnv):
        env = multi_agent_to_single_agent(env)
    # wrap for video recording
    if args_cli.video:
        video_kwargs = {
            "video_folder": os.path.join(log_dir, "videos", "train"),
            "step_trigger": lambda step: step % args_cli.video_interval == 0,
            "video_length": args_cli.video_length,
            "disable_logger": True,
        }
        print("[INFO] Recording videos during training.")
        print_dict(video_kwargs, nesting=4)
        env = gym.wrappers.RecordVideo(env, **video_kwargs)
    # wrap around environment for stable baselines
    env = Sb3VecEnvWrapper(env, fast_variant=not args_cli.keep_all_info)
    norm_keys = {"normalize_input", "normalize_value", "clip_obs"}
    norm_args = {}
    for key in norm_keys:
        if key in agent_cfg:
            norm_args[key] = agent_cfg.pop(key)
    if norm_args and norm_args.get("normalize_input"):
        print(f"Normalizing input, {norm_args=}")
        env = VecNormalize(
            env,
            training=True,
            norm_obs=norm_args["normalize_input"],
            norm_reward=norm_args.get("normalize_value", False),
            clip_obs=norm_args.get("clip_obs", 100.0),
            gamma=agent_cfg["gamma"],
            clip_reward=np.inf,
        )
    # create agent from stable baselines
    agent = PPO(policy_arch, env, verbose=1, tensorboard_log=log_dir, **agent_cfg)
    if args_cli.checkpoint is not None:
        agent = agent.load(args_cli.checkpoint, env, print_system_info=True)
    # callbacks for agent
    checkpoint_callback = CheckpointCallback(save_freq=1000, save_path=log_dir, name_prefix="model", verbose=2)
    callbacks = [checkpoint_callback, LogEveryNTimesteps(n_steps=args_cli.log_interval)]
    # train the agent
    with contextlib.suppress(KeyboardInterrupt):
        agent.learn(
            total_timesteps=n_timesteps,
            callback=callbacks,
            progress_bar=True,
            log_interval=None,
        )
    # save the final model
    agent.save(os.path.join(log_dir, "model"))
    print("Saving to:")
    print(os.path.join(log_dir, "model.zip"))
    if isinstance(env, VecNormalize):
        print("Saving normalization")
        env.save(os.path.join(log_dir, "model_vecnormalize.pkl"))
    # close the simulator
    env.close()
if __name__ == "__main__":
    # run the main function
    main()
    # close sim app
    simulation_app.close()

The argument --agent is used to specify the learning library to use. This is used to retrieve the configuration instance from the kwargs dictionary. You can manually specify alternate configuration instances by passing the --agent argument.

The Code Execution

Since for the cartpole balancing task, RSL-RL library offers two configuration instances, we can use the --agent argument to specify the configuration instance to use.

Training with the standard PPO configuration:

# standard PPO training
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --headless \
  --run_name ppo

Training with the PPO configuration with symmetry augmentation:

# 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

The --run_name argument is used to specify the name of the run. This is used to create a directory for the run in the logs/rsl_rl/cartpole directory.