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(
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 export flag if requested
147 if isinstance(env_cfg, ManagerBasedRLEnvCfg):
148 env_cfg.export_io_descriptors = args_cli.export_io_descriptors
149 else:
150 omni.log.warn(
151 "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported."
152 )
153
154 # set the log directory for the environment (works for all environment types)
155 env_cfg.log_dir = log_dir
156
157 # create isaac environment
158 env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
159
160 # convert to single-agent instance if required by the RL algorithm
161 if isinstance(env.unwrapped, DirectMARLEnv):
162 env = multi_agent_to_single_agent(env)
163
164 # wrap for video recording
165 if args_cli.video:
166 video_kwargs = {
167 "video_folder": os.path.join(log_dir, "videos", "train"),
168 "step_trigger": lambda step: step % args_cli.video_interval == 0,
169 "video_length": args_cli.video_length,
170 "disable_logger": True,
171 }
172 print("[INFO] Recording videos during training.")
173 print_dict(video_kwargs, nesting=4)
174 env = gym.wrappers.RecordVideo(env, **video_kwargs)
175
176 # wrap around environment for stable baselines
177 env = Sb3VecEnvWrapper(env, fast_variant=not args_cli.keep_all_info)
178
179 norm_keys = {"normalize_input", "normalize_value", "clip_obs"}
180 norm_args = {}
181 for key in norm_keys:
182 if key in agent_cfg:
183 norm_args[key] = agent_cfg.pop(key)
184
185 if norm_args and norm_args.get("normalize_input"):
186 print(f"Normalizing input, {norm_args=}")
187 env = VecNormalize(
188 env,
189 training=True,
190 norm_obs=norm_args["normalize_input"],
191 norm_reward=norm_args.get("normalize_value", False),
192 clip_obs=norm_args.get("clip_obs", 100.0),
193 gamma=agent_cfg["gamma"],
194 clip_reward=np.inf,
195 )
196
197 # create agent from stable baselines
198 agent = PPO(policy_arch, env, verbose=1, tensorboard_log=log_dir, **agent_cfg)
199 if args_cli.checkpoint is not None:
200 agent = agent.load(args_cli.checkpoint, env, print_system_info=True)
201
202 # callbacks for agent
203 checkpoint_callback = CheckpointCallback(save_freq=1000, save_path=log_dir, name_prefix="model", verbose=2)
204 callbacks = [checkpoint_callback, LogEveryNTimesteps(n_steps=args_cli.log_interval)]
205
206 # train the agent
207 with contextlib.suppress(KeyboardInterrupt):
208 agent.learn(
209 total_timesteps=n_timesteps,
210 callback=callbacks,
211 progress_bar=True,
212 log_interval=None,
213 )
214 # save the final model
215 agent.save(os.path.join(log_dir, "model"))
216 print("Saving to:")
217 print(os.path.join(log_dir, "model.zip"))
218
219 if isinstance(env, VecNormalize):
220 print("Saving normalization")
221 env.save(os.path.join(log_dir, "model_vecnormalize.pkl"))
222
223 # close the simulator
224 env.close()
225
226
227if __name__ == "__main__":
228 # run the main function
229 main()
230 # close sim app
231 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:
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.wrappers.sb3.Sb3VecEnvWrapper
: This wrapper converts the environment into a Stable-Baselines3 compatible environment.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.