Training with an RL Agent#

In the previous tutorials, we covered how to define an RL task environment, register it into the gym registry, and interact with it using a random agent. We now move on to the next step: training an RL agent to solve the task.

Although the envs.ManagerBasedRLEnv conforms to the gymnasium.Env interface, it is not exactly a gym environment. The input and outputs of the environment are not numpy arrays, but rather based on torch tensors with the first dimension being the number of environment instances.

Additionally, most RL libraries expect their own variation of an environment interface. For example, Stable-Baselines3 expects the environment to conform to its VecEnv API which expects a list of numpy arrays instead of a single tensor. Similarly, RSL-RL, RL-Games and SKRL expect a different interface. Since there is no one-size-fits-all solution, we do not base the envs.ManagerBasedRLEnv on any particular learning library. Instead, we implement wrappers to convert the environment into the expected interface. These are specified in the isaaclab_rl module.

In this tutorial, we will use Stable-Baselines3 to train an RL agent to solve the cartpole balancing task.

Caution

Wrapping the environment with the respective learning framework’s wrapper should happen in the end, i.e. after all other wrappers have been applied. This is because the learning framework’s wrapper modifies the interpretation of environment’s APIs which may no longer be compatible with gymnasium.Env.

The Code#

For this tutorial, we use the training script from Stable-Baselines3 workflow in the scripts/reinforcement_learning/sb3 directory.

Code for train.py
  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("--seed", type=int, default=None, help="Seed used for the environment")
 27parser.add_argument("--log_interval", type=int, default=100_000, help="Log data every n timesteps.")
 28parser.add_argument("--checkpoint", type=str, default=None, help="Continue the training from checkpoint.")
 29parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
 30parser.add_argument(
 31    "--keep_all_info",
 32    action="store_true",
 33    default=False,
 34    help="Use a slower SB3 wrapper but keep all the extra training info.",
 35)
 36# append AppLauncher cli args
 37AppLauncher.add_app_launcher_args(parser)
 38# parse the arguments
 39args_cli, hydra_args = parser.parse_known_args()
 40# always enable cameras to record video
 41if args_cli.video:
 42    args_cli.enable_cameras = True
 43
 44# clear out sys.argv for Hydra
 45sys.argv = [sys.argv[0]] + hydra_args
 46
 47# launch omniverse app
 48app_launcher = AppLauncher(args_cli)
 49simulation_app = app_launcher.app
 50
 51
 52def cleanup_pbar(*args):
 53    """
 54    A small helper to stop training and
 55    cleanup progress bar properly on ctrl+c
 56    """
 57    import gc
 58
 59    tqdm_objects = [obj for obj in gc.get_objects() if "tqdm" in type(obj).__name__]
 60    for tqdm_object in tqdm_objects:
 61        if "tqdm_rich" in type(tqdm_object).__name__:
 62            tqdm_object.close()
 63    raise KeyboardInterrupt
 64
 65
 66# disable KeyboardInterrupt override
 67signal.signal(signal.SIGINT, cleanup_pbar)
 68
 69"""Rest everything follows."""
 70
 71import gymnasium as gym
 72import numpy as np
 73import os
 74import random
 75from datetime import datetime
 76
 77from stable_baselines3 import PPO
 78from stable_baselines3.common.callbacks import CheckpointCallback, LogEveryNTimesteps
 79from stable_baselines3.common.vec_env import VecNormalize
 80
 81from isaaclab.envs import (
 82    DirectMARLEnv,
 83    DirectMARLEnvCfg,
 84    DirectRLEnvCfg,
 85    ManagerBasedRLEnvCfg,
 86    multi_agent_to_single_agent,
 87)
 88from isaaclab.utils.dict import print_dict
 89from isaaclab.utils.io import dump_pickle, dump_yaml
 90
 91from isaaclab_rl.sb3 import Sb3VecEnvWrapper, process_sb3_cfg
 92
 93import isaaclab_tasks  # noqa: F401
 94from isaaclab_tasks.utils.hydra import hydra_task_config
 95
 96# PLACEHOLDER: Extension template (do not remove this comment)
 97
 98
 99@hydra_task_config(args_cli.task, "sb3_cfg_entry_point")
100def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict):
101    """Train with stable-baselines agent."""
102    # randomly sample a seed if seed = -1
103    if args_cli.seed == -1:
104        args_cli.seed = random.randint(0, 10000)
105
106    # override configurations with non-hydra CLI arguments
107    env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
108    agent_cfg["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"]
109    # max iterations for training
110    if args_cli.max_iterations is not None:
111        agent_cfg["n_timesteps"] = args_cli.max_iterations * agent_cfg["n_steps"] * env_cfg.scene.num_envs
112
113    # set the environment seed
114    # note: certain randomizations occur in the environment initialization so we set the seed here
115    env_cfg.seed = agent_cfg["seed"]
116    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
117
118    # directory for logging into
119    run_info = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
120    log_root_path = os.path.abspath(os.path.join("logs", "sb3", args_cli.task))
121    print(f"[INFO] Logging experiment in directory: {log_root_path}")
122    # The Ray Tune workflow extracts experiment name using the logging line below, hence, do not change it (see PR #2346, comment-2819298849)
123    print(f"Exact experiment name requested from command line: {run_info}")
124    log_dir = os.path.join(log_root_path, run_info)
125    # dump the configuration into log-directory
126    dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg)
127    dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg)
128    dump_pickle(os.path.join(log_dir, "params", "env.pkl"), env_cfg)
129    dump_pickle(os.path.join(log_dir, "params", "agent.pkl"), agent_cfg)
130
131    # save command used to run the script
132    command = " ".join(sys.orig_argv)
133    (Path(log_dir) / "command.txt").write_text(command)
134
135    # post-process agent configuration
136    agent_cfg = process_sb3_cfg(agent_cfg, env_cfg.scene.num_envs)
137    # read configurations about the agent-training
138    policy_arch = agent_cfg.pop("policy")
139    n_timesteps = agent_cfg.pop("n_timesteps")
140
141    # create isaac environment
142    env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
143
144    # convert to single-agent instance if required by the RL algorithm
145    if isinstance(env.unwrapped, DirectMARLEnv):
146        env = multi_agent_to_single_agent(env)
147
148    # wrap for video recording
149    if args_cli.video:
150        video_kwargs = {
151            "video_folder": os.path.join(log_dir, "videos", "train"),
152            "step_trigger": lambda step: step % args_cli.video_interval == 0,
153            "video_length": args_cli.video_length,
154            "disable_logger": True,
155        }
156        print("[INFO] Recording videos during training.")
157        print_dict(video_kwargs, nesting=4)
158        env = gym.wrappers.RecordVideo(env, **video_kwargs)
159
160    # wrap around environment for stable baselines
161    env = Sb3VecEnvWrapper(env, fast_variant=not args_cli.keep_all_info)
162
163    norm_keys = {"normalize_input", "normalize_value", "clip_obs"}
164    norm_args = {}
165    for key in norm_keys:
166        if key in agent_cfg:
167            norm_args[key] = agent_cfg.pop(key)
168
169    if norm_args and norm_args.get("normalize_input"):
170        print(f"Normalizing input, {norm_args=}")
171        env = VecNormalize(
172            env,
173            training=True,
174            norm_obs=norm_args["normalize_input"],
175            norm_reward=norm_args.get("normalize_value", False),
176            clip_obs=norm_args.get("clip_obs", 100.0),
177            gamma=agent_cfg["gamma"],
178            clip_reward=np.inf,
179        )
180
181    # create agent from stable baselines
182    agent = PPO(policy_arch, env, verbose=1, tensorboard_log=log_dir, **agent_cfg)
183    if args_cli.checkpoint is not None:
184        agent = agent.load(args_cli.checkpoint, env, print_system_info=True)
185
186    # callbacks for agent
187    checkpoint_callback = CheckpointCallback(save_freq=1000, save_path=log_dir, name_prefix="model", verbose=2)
188    callbacks = [checkpoint_callback, LogEveryNTimesteps(n_steps=args_cli.log_interval)]
189
190    # train the agent
191    with contextlib.suppress(KeyboardInterrupt):
192        agent.learn(
193            total_timesteps=n_timesteps,
194            callback=callbacks,
195            progress_bar=True,
196            log_interval=None,
197        )
198    # save the final model
199    agent.save(os.path.join(log_dir, "model"))
200    print("Saving to:")
201    print(os.path.join(log_dir, "model.zip"))
202
203    if isinstance(env, VecNormalize):
204        print("Saving normalization")
205        env.save(os.path.join(log_dir, "model_vecnormalize.pkl"))
206
207    # close the simulator
208    env.close()
209
210
211if __name__ == "__main__":
212    # run the main function
213    main()
214    # close sim app
215    simulation_app.close()

The Code Explained#

Most of the code above is boilerplate code to create logging directories, saving the parsed configurations, and setting up different Stable-Baselines3 components. For this tutorial, the important part is creating the environment and wrapping it with the Stable-Baselines3 wrapper.

There are three wrappers used in the code above:

  1. gymnasium.wrappers.RecordVideo: This wrapper records a video of the environment and saves it to the specified directory. This is useful for visualizing the agent’s behavior during training.

  2. wrappers.sb3.Sb3VecEnvWrapper: This wrapper converts the environment into a Stable-Baselines3 compatible environment.

  3. stable_baselines3.common.vec_env.VecNormalize: This wrapper normalizes the environment’s observations and rewards.

Each of these wrappers wrap around the previous wrapper by following env = wrapper(env, *args, **kwargs) repeatedly. The final environment is then used to train the agent. For more information on how these wrappers work, please refer to the Wrapping environments documentation.

The Code Execution#

We train a PPO agent from Stable-Baselines3 to solve the cartpole balancing task.

Training the agent#

There are three main ways to train the agent. Each of them has their own advantages and disadvantages. It is up to you to decide which one you prefer based on your use case.

Headless execution#

If the --headless flag is set, the simulation is not rendered during training. This is useful when training on a remote server or when you do not want to see the simulation. Typically, it speeds up the training process since only physics simulation step is performed.

./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Cartpole-v0 --num_envs 64 --headless

Headless execution with off-screen render#

Since the above command does not render the simulation, it is not possible to visualize the agent’s behavior during training. To visualize the agent’s behavior, we pass the --enable_cameras which enables off-screen rendering. Additionally, we pass the flag --video which records a video of the agent’s behavior during training.

./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Cartpole-v0 --num_envs 64 --headless --video

The videos are saved to the logs/sb3/Isaac-Cartpole-v0/<run-dir>/videos/train directory. You can open these videos using any video player.

Interactive execution#

While the above two methods are useful for training the agent, they don’t allow you to interact with the simulation to see what is happening. In this case, you can ignore the --headless flag and run the training script as follows:

./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Cartpole-v0 --num_envs 64

This will open the Isaac Sim window and you can see the agent training in the environment. However, this will slow down the training process since the simulation is rendered on the screen. As a workaround, you can switch between different render modes in the "Isaac Lab" window that is docked on the bottom-right corner of the screen. To learn more about these render modes, please check the sim.SimulationContext.RenderMode class.

Viewing the logs#

On a separate terminal, you can monitor the training progress by executing the following command:

# execute from the root directory of the repository
./isaaclab.sh -p -m tensorboard.main --logdir logs/sb3/Isaac-Cartpole-v0

Playing the trained agent#

Once the training is complete, you can visualize the trained agent by executing the following command:

# execute from the root directory of the repository
./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Cartpole-v0 --num_envs 32 --use_last_checkpoint

The above command will load the latest checkpoint from the logs/sb3/Isaac-Cartpole-v0 directory. You can also specify a specific checkpoint by passing the --checkpoint flag.