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