> This page is for version Release 2.2. > For other versions, use one of these documentation indexes: > - Release 3.0 (default): https://docs.nvidia.com/isaaclab/3.0/llms.txt > - Release 2.3: https://docs.nvidia.com/isaaclab/2.3/llms.txt > - Release 2.2: https://docs.nvidia.com/isaaclab/2.2/llms.txt > - Release 2.1: https://docs.nvidia.com/isaaclab/2.1/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/isaaclab/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/isaaclab/_mcp/server. # Creating a Manager-Based RL Environment Having learnt how to create a base environment in [Creating a Manager-Based Base Environment](/source/tutorials/03_envs/create_manager_base_env), we will now look at how to create a manager-based task environment for reinforcement learning. The base environment is designed as an sense-act environment where the agent can send commands to the environment and receive observations from the environment. This minimal interface is sufficient for many applications such as traditional motion planning and controls. However, many applications require a task-specification which often serves as the learning objective for the agent. For instance, in a navigation task, the agent may be required to reach a goal location. To this end, we use the `envs.ManagerBasedRLEnv` class which extends the base environment to include a task specification. Similar to other components in Isaac Lab, instead of directly modifying the base class `envs.ManagerBasedRLEnv`, we encourage users to simply implement a configuration `envs.ManagerBasedRLEnvCfg` for their task environment. This practice allows us to separate the task specification from the environment implementation, making it easier to reuse components of the same environment for different tasks. In this tutorial, we will configure the cartpole environment using the `envs.ManagerBasedRLEnvCfg` to create a manager-based task for balancing the pole upright. We will learn how to specify the task using reward terms, termination criteria, curriculum and commands. ## The Code For this tutorial, we use the cartpole environment defined in `isaaclab_tasks.manager_based.classic.cartpole` module. ```python # 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 import math import isaaclab.sim as sim_utils from isaaclab.assets import ArticulationCfg, AssetBaseCfg from isaaclab.envs import ManagerBasedRLEnvCfg from isaaclab.managers import EventTermCfg as EventTerm from isaaclab.managers import ObservationGroupCfg as ObsGroup from isaaclab.managers import ObservationTermCfg as ObsTerm from isaaclab.managers import RewardTermCfg as RewTerm from isaaclab.managers import SceneEntityCfg from isaaclab.managers import TerminationTermCfg as DoneTerm from isaaclab.scene import InteractiveSceneCfg from isaaclab.utils import configclass import isaaclab_tasks.manager_based.classic.cartpole.mdp as mdp ## # Pre-defined configs ## from isaaclab_assets.robots.cartpole import CARTPOLE_CFG # isort:skip ## # Scene definition ## @configclass class CartpoleSceneCfg(InteractiveSceneCfg): """Configuration for a cart-pole scene.""" # ground plane ground = AssetBaseCfg( prim_path="/World/ground", spawn=sim_utils.GroundPlaneCfg(size=(100.0, 100.0)), ) # cartpole robot: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # lights dome_light = AssetBaseCfg( prim_path="/World/DomeLight", spawn=sim_utils.DomeLightCfg(color=(0.9, 0.9, 0.9), intensity=500.0), ) ## # MDP settings ## @configclass class ActionsCfg: """Action specifications for the MDP.""" joint_effort = mdp.JointEffortActionCfg(asset_name="robot", joint_names=["slider_to_cart"], scale=100.0) @configclass class ObservationsCfg: """Observation specifications for the MDP.""" @configclass class PolicyCfg(ObsGroup): """Observations for policy group.""" # observation terms (order preserved) joint_pos_rel = ObsTerm(func=mdp.joint_pos_rel) joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel) def __post_init__(self) -> None: self.enable_corruption = False self.concatenate_terms = True # observation groups policy: PolicyCfg = PolicyCfg() @configclass class EventCfg: """Configuration for events.""" # reset reset_cart_position = EventTerm( func=mdp.reset_joints_by_offset, mode="reset", params={ "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), "position_range": (-1.0, 1.0), "velocity_range": (-0.5, 0.5), }, ) reset_pole_position = EventTerm( func=mdp.reset_joints_by_offset, mode="reset", params={ "asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), "position_range": (-0.25 * math.pi, 0.25 * math.pi), "velocity_range": (-0.25 * math.pi, 0.25 * math.pi), }, ) @configclass class RewardsCfg: """Reward terms for the MDP.""" # (1) Constant running reward alive = RewTerm(func=mdp.is_alive, weight=1.0) # (2) Failure penalty terminating = RewTerm(func=mdp.is_terminated, weight=-2.0) # (3) Primary task: keep pole upright pole_pos = RewTerm( func=mdp.joint_pos_target_l2, weight=-1.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), "target": 0.0}, ) # (4) Shaping tasks: lower cart velocity cart_vel = RewTerm( func=mdp.joint_vel_l1, weight=-0.01, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"])}, ) # (5) Shaping tasks: lower pole angular velocity pole_vel = RewTerm( func=mdp.joint_vel_l1, weight=-0.005, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"])}, ) @configclass class TerminationsCfg: """Termination terms for the MDP.""" # (1) Time out time_out = DoneTerm(func=mdp.time_out, time_out=True) # (2) Cart out of bounds cart_out_of_bounds = DoneTerm( func=mdp.joint_pos_out_of_manual_limit, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), "bounds": (-3.0, 3.0)}, ) ## # Environment configuration ## @configclass class CartpoleEnvCfg(ManagerBasedRLEnvCfg): """Configuration for the cartpole environment.""" # Scene settings scene: CartpoleSceneCfg = CartpoleSceneCfg(num_envs=4096, env_spacing=4.0, clone_in_fabric=True) # Basic settings observations: ObservationsCfg = ObservationsCfg() actions: ActionsCfg = ActionsCfg() events: EventCfg = EventCfg() # MDP settings rewards: RewardsCfg = RewardsCfg() terminations: TerminationsCfg = TerminationsCfg() # Post initialization def __post_init__(self) -> None: """Post initialization.""" # general settings self.decimation = 2 self.episode_length_s = 5 # viewer settings self.viewer.eye = (8.0, 0.0, 5.0) # simulation settings self.sim.dt = 1 / 120 self.sim.render_interval = self.decimation ``` The script for running the environment `run_cartpole_rl_env.py` is present in the `isaaclab/scripts/tutorials/03_envs` directory. The script is similar to the `cartpole_base_env.py` script in the previous tutorial, except that it uses the `envs.ManagerBasedRLEnv` instead of the `envs.ManagerBasedEnv`. ```python # 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 """ This script demonstrates how to run the RL environment for the cartpole balancing task. ~~~bash ./isaaclab.sh -p scripts/tutorials/03_envs/run_cartpole_rl_env.py --num_envs 32 ~~~ """ """Launch Isaac Sim Simulator first.""" import argparse from isaaclab.app import AppLauncher # add argparse arguments parser = argparse.ArgumentParser(description="Tutorial on running the cartpole RL environment.") parser.add_argument("--num_envs", type=int, default=16, help="Number of environments to spawn.") # append AppLauncher cli args AppLauncher.add_app_launcher_args(parser) # parse the arguments args_cli = parser.parse_args() # launch omniverse app app_launcher = AppLauncher(args_cli) simulation_app = app_launcher.app """Rest everything follows.""" import torch from isaaclab.envs import ManagerBasedRLEnv from isaaclab_tasks.manager_based.classic.cartpole.cartpole_env_cfg import CartpoleEnvCfg def main(): """Main function.""" # create environment configuration env_cfg = CartpoleEnvCfg() env_cfg.scene.num_envs = args_cli.num_envs env_cfg.sim.device = args_cli.device # setup RL environment env = ManagerBasedRLEnv(cfg=env_cfg) # simulate physics count = 0 while simulation_app.is_running(): with torch.inference_mode(): # reset if count % 300 == 0: count = 0 env.reset() print("-" * 80) print("[INFO]: Resetting environment...") # sample random actions joint_efforts = torch.randn_like(env.action_manager.action) # step the environment obs, rew, terminated, truncated, info = env.step(joint_efforts) # print current orientation of pole print("[Env 0]: Pole joint: ", obs["policy"][0][1].item()) # update counter count += 1 # close the environment env.close() if __name__ == "__main__": # run the main function main() # close sim app simulation_app.close() ``` ## The Code Explained We already went through parts of the above in the [Creating a Manager-Based Base Environment](/source/tutorials/03_envs/create_manager_base_env) tutorial to learn about how to specify the scene, observations, actions and events. Thus, in this tutorial, we will focus only on the RL components of the environment. In Isaac Lab, we provide various implementations of different terms in the `envs.mdp` module. We will use some of these terms in this tutorial, but users are free to define their own terms as well. These are usually placed in their task-specific sub-package (for instance, in `isaaclab_tasks.manager_based.classic.cartpole.mdp`). ### Defining rewards The `managers.RewardManager` is used to compute the reward terms for the agent. Similar to the other managers, its terms are configured using the `managers.RewardTermCfg` class. The `managers.RewardTermCfg` class specifies the function or callable class that computes the reward as well as the weighting associated with it. It also takes in dictionary of arguments, `"params"` that are passed to the reward function when it is called. For the cartpole task, we will use the following reward terms: * **Alive Reward**: Encourage the agent to stay alive for as long as possible. * **Terminating Reward**: Similarly penalize the agent for terminating. * **Pole Angle Reward**: Encourage the agent to keep the pole at the desired upright position. * **Cart Velocity Reward**: Encourage the agent to keep the cart velocity as small as possible. * **Pole Velocity Reward**: Encourage the agent to keep the pole velocity as small as possible. ```python @configclass class RewardsCfg: """Reward terms for the MDP.""" # (1) Constant running reward alive = RewTerm(func=mdp.is_alive, weight=1.0) # (2) Failure penalty terminating = RewTerm(func=mdp.is_terminated, weight=-2.0) # (3) Primary task: keep pole upright pole_pos = RewTerm( func=mdp.joint_pos_target_l2, weight=-1.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), "target": 0.0}, ) # (4) Shaping tasks: lower cart velocity cart_vel = RewTerm( func=mdp.joint_vel_l1, weight=-0.01, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"])}, ) # (5) Shaping tasks: lower pole angular velocity pole_vel = RewTerm( func=mdp.joint_vel_l1, weight=-0.005, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"])}, ) ``` ### Defining termination criteria Most learning tasks happen over a finite number of steps that we call an episode. For instance, in the cartpole task, we want the agent to balance the pole for as long as possible. However, if the agent reaches an unstable or unsafe state, we want to terminate the episode. On the other hand, if the agent is able to balance the pole for a long time, we want to terminate the episode and start a new one so that the agent can learn to balance the pole from a different starting configuration. The `managers.TerminationsCfg` configures what constitutes for an episode to terminate. In this example, we want the task to terminate when either of the following conditions is met: * **Episode Length** The episode length is greater than the defined max_episode_length * **Cart out of bounds** The cart goes outside of the bounds \[-3, 3] The flag `managers.TerminationsCfg.time_out` specifies whether the term is a time-out (truncation) term or terminated term. These are used to indicate the two types of terminations as described in [Gymnasium's documentation](https://gymnasium.farama.org/tutorials/gymnasium_basics/handling_time_limits/). ```python @configclass class TerminationsCfg: """Termination terms for the MDP.""" # (1) Time out time_out = DoneTerm(func=mdp.time_out, time_out=True) # (2) Cart out of bounds cart_out_of_bounds = DoneTerm( func=mdp.joint_pos_out_of_manual_limit, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), "bounds": (-3.0, 3.0)}, ) ``` ### Defining commands For various goal-conditioned tasks, it is useful to specify the goals or commands for the agent. These are handled through the `managers.CommandManager`. The command manager handles resampling and updating the commands at each step. It can also be used to provide the commands as an observation to the agent. For this simple task, we do not use any commands. Hence, we leave this attribute as its default value, which is None. You can see an example of how to define a command manager in the other locomotion or manipulation tasks. ### Defining curriculum Often times when training a learning agent, it helps to start with a simple task and gradually increase the tasks's difficulty as the agent training progresses. This is the idea behind curriculum learning. In Isaac Lab, we provide a `managers.CurriculumManager` class that can be used to define a curriculum for your environment. In this tutorial we don't implement a curriculum for simplicity, but you can see an example of a curriculum definition in the other locomotion or manipulation tasks. ### Tying it all together With all the above components defined, we can now create the `ManagerBasedRLEnvCfg` configuration for the cartpole environment. This is similar to the `ManagerBasedEnvCfg` defined in [Creating a Manager-Based Base Environment](/source/tutorials/03_envs/create_manager_base_env), only with the added RL components explained in the above sections. ```python @configclass class CartpoleEnvCfg(ManagerBasedRLEnvCfg): """Configuration for the cartpole environment.""" # Scene settings scene: CartpoleSceneCfg = CartpoleSceneCfg(num_envs=4096, env_spacing=4.0, clone_in_fabric=True) # Basic settings observations: ObservationsCfg = ObservationsCfg() actions: ActionsCfg = ActionsCfg() events: EventCfg = EventCfg() # MDP settings rewards: RewardsCfg = RewardsCfg() terminations: TerminationsCfg = TerminationsCfg() # Post initialization def __post_init__(self) -> None: """Post initialization.""" # general settings self.decimation = 2 self.episode_length_s = 5 # viewer settings self.viewer.eye = (8.0, 0.0, 5.0) # simulation settings self.sim.dt = 1 / 120 self.sim.render_interval = self.decimation ``` ### Running the simulation loop Coming back to the `run_cartpole_rl_env.py` script, the simulation loop is similar to the previous tutorial. The only difference is that we create an instance of `envs.ManagerBasedRLEnv` instead of the `envs.ManagerBasedEnv`. Consequently, now the `envs.ManagerBasedRLEnv.step` method returns additional signals such as the reward and termination status. The information dictionary also maintains logging of quantities such as the reward contribution from individual terms, the termination status of each term, the episode length etc. ```python def main(): """Main function.""" # create environment configuration env_cfg = CartpoleEnvCfg() env_cfg.scene.num_envs = args_cli.num_envs env_cfg.sim.device = args_cli.device # setup RL environment env = ManagerBasedRLEnv(cfg=env_cfg) # simulate physics count = 0 while simulation_app.is_running(): with torch.inference_mode(): # reset if count % 300 == 0: count = 0 env.reset() print("-" * 80) print("[INFO]: Resetting environment...") # sample random actions joint_efforts = torch.randn_like(env.action_manager.action) # step the environment obs, rew, terminated, truncated, info = env.step(joint_efforts) # print current orientation of pole print("[Env 0]: Pole joint: ", obs["policy"][0][1].item()) # update counter count += 1 # close the environment env.close() ``` ## The Code Execution Similar to the previous tutorial, we can run the environment by executing the `run_cartpole_rl_env.py` script. ```bash ./isaaclab.sh -p scripts/tutorials/03_envs/run_cartpole_rl_env.py --num_envs 32 ``` This should open a similar simulation as in the previous tutorial. However, this time, the environment returns more signals that specify the reward and termination status. Additionally, the individual environments reset themselves when they terminate based on the termination criteria specified in the configuration. ![result of run\_cartpole\_rl\_env.py](/isaaclab/_fern-img/542f184bac0597be137e0a1b49796321823650f97c0f84ed731eb2529bb9d95d.webp) To stop the simulation, you can either close the window, or press `Ctrl+C` in the terminal where you started the simulation. In this tutorial, we learnt how to create a task environment for reinforcement learning. We do this by extending the base environment to include the rewards, terminations, commands and curriculum terms. We also learnt how to use the `envs.ManagerBasedRLEnv` class to run the environment and receive various signals from it. While it is possible to manually create an instance of `envs.ManagerBasedRLEnv` class for a desired task, this is not scalable as it requires specialized scripts for each task. Thus, we exploit the `gymnasium.make` function to create the environment with the gym interface. We will learn how to do this in the next tutorial.