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# Modifying an existing Direct RL Environment

Having learnt how to create a task in [Creating a Direct Workflow RL Environment](/source/how-to/create_direct_rl_env), register it in [Registering an Environment](/source/how-to/register_rl_env_gym),
and train it in [Training with an RL Agent](/source/how-to/run_rl_training), we will now look at how to make minor modifications to an existing task.

Sometimes it is necessary to create, due to complexity or variations from existing examples, tasks from scratch. However, in certain situations,
it is possible to start from the existing code and introduce minor changes, one by one, to transform them according to our needs.

In this tutorial, we will make minor modifications to the direct workflow Humanoid task to change the simple
humanoid model to the Unitree H1 humanoid robot without affecting the original code.

## The Base Code

For this tutorial, we start from the direct workflow Humanoid environment defined in `isaaclab_tasks.core.locomotion.humanoid` module.

```python
# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

"""Direct-workflow Humanoid locomotion environment."""

from __future__ import annotations

from ..locomotion_direct_env import LocomotionDirectEnv
from .humanoid_direct_env_cfg import HumanoidEnvCfg

class HumanoidEnv(LocomotionDirectEnv):
    """Direct-workflow Humanoid locomotion environment.

    The behavior is fully defined by `LocomotionDirectEnv` and `HumanoidEnvCfg`.
    """

    cfg: HumanoidEnvCfg
```

## The Changes Explained

### Deriving and registering a new task

To avoid modifying the existing task, create an `h1_env.py` file in the Isaac Lab project
`source/isaaclab_tasks/isaaclab_tasks/core/locomotion/humanoid`
folder. The new classes below inherit the existing environment and configuration, changing only the robot-specific data.

Register the derived task under the name `Isaac-H1-Direct-v0`.
To do this, we modify the `__init__.py` file in the same working folder and add the following entry.
Refer to the [Registering an Environment](/source/how-to/register_rl_env_gym) tutorial for more details about environment registrations.

> **Tip**
>
> If the changes in the task are minimal, it is very likely that the same RL library agent configurations can be used to train it successfully.
> Otherwise, it is advisable to create new configuration files (adjusting their name during registration under the `kwargs` parameter)
> to avoid altering the original configurations.

```python
from .h1_env import H1Env, H1EnvCfg
```

```python
gym.register(
    id="Isaac-H1-Direct-v0",
    entry_point="isaaclab_tasks.core.locomotion.humanoid:H1Env",
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": H1EnvCfg,
        "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_direct_ppo_cfg.yaml",
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:HumanoidDirectPPORunnerCfg",
        "default_agent": "rsl_rl",
        "skrl_cfg_entry_point": f"{agents.__name__}:skrl_direct_ppo_cfg.yaml",
    },
)
```

### Changing the robot

The `H1SceneCfg` class (in the newly created `h1_env.py` file) declares the assets that the
environment constructs. In this example, its `robot` property holds the target articulation configuration.

Since the Unitree H1 robot is included in the Isaac Lab assets extension (`isaaclab_assets`) we can just import it
and do the replacement directly (under the `H1SceneCfg.robot` property), as shown below. Note that we also need to modify the
`joint_gears` property as it holds robot-specific configuration values.

> **Tip**
>
> If the target robot is not included in the Isaac Lab assets extension, it is possible to load and configure it, from a USD file,
> by using the `~isaaclab.assets.ArticulationCfg` class.
>
> * See the [Isaac-Open-Drawer-Franka-Direct](https://github.com/isaac-sim/IsaacLab/blob/release/3.0.0/source/isaaclab_tasks/isaaclab_tasks/core/cabinet/cabinet_direct_env.py) source code for an example of loading and configuring a robot from a USD file.
> * Refer to the [Importing a New Asset ](/source/how-to/import_new_asset) tutorial for details on how to import an asset from URDF or MJCF file, and other formats.

```python
from isaaclab.assets import ArticulationCfg
from isaaclab.utils import configclass, replace
from isaaclab_assets import H1_CFG

from isaaclab_tasks.core.locomotion.humanoid.humanoid_direct_env import HumanoidEnv
from isaaclab_tasks.core.locomotion.humanoid.humanoid_direct_env_cfg import HumanoidDirectSceneCfg, HumanoidEnvCfg
```

```python
@configclass
class H1SceneCfg(HumanoidDirectSceneCfg):
    robot: ArticulationCfg = replace(H1_CFG, prim_path="{ENV_REGEX_NS}/Robot")
```

The robot changed, and with it the number of joints to control or the number of rigid bodies that compose the articulation, for example.
Therefore, define the derived environment configuration and adjust the values that depend on the robot, such as the action and
observation spaces. The environment implementation itself remains unchanged.

The H1 has 19 controlled joints. The inherited observation function combines 12 root and target
values, 19 joint positions, 19 joint velocities, 19 actions, and 12 wrench values from its two feet,
for 81 values. Set `feet_body_names` to match H1's ankle links so the joint-wrench sensor selects
the correct bodies instead of the base Humanoid's foot names.

```python
@configclass
class H1EnvCfg(HumanoidEnvCfg):
    action_space = 19
    observation_space = 81
    scene: H1SceneCfg = H1SceneCfg(num_envs=4096, env_spacing=5.0, replicate_physics=True, clone_in_fabric=True)
    # the gears are keyed by joint name expression, so they are independent of the joint ordering
    joint_gears: dict[str, float] = {".*": 50.0}
    feet_body_names: list[str] = [".*ankle_link"]

class H1Env(HumanoidEnv):
    cfg: H1EnvCfg
```

## The Code Execution

After the minor modification has been done, and similar to the previous tutorial, we can train on the task using one of the available RL workflows for such task.

#### uv (Recommended)

```bash
uv run --extra rl-games isaaclab train --rl_library rl_games --task Isaac-H1-Direct-v0
```

#### isaaclab.sh / isaaclab.bat

```bash
./isaaclab.sh train --rl_library rl_games --task Isaac-H1-Direct-v0
```

When the training is finished, we can visualize the result with the following command.
To stop the simulation, you can either close the window, or press `Ctrl+C` in the terminal
where you started the simulation.

#### uv (Recommended)

```bash
uv run --extra rl-games isaaclab play --rl_library rl_games --task Isaac-H1-Direct-v0 --num_envs 64 --viz kit
```

#### isaaclab.sh / isaaclab.bat

```bash
./isaaclab.sh play --rl_library rl_games --task Isaac-H1-Direct-v0 --num_envs 64 --viz kit
```

![result of training Isaac-H1-Direct-v0 task](/isaaclab/_fern-img/6cba4a94f707e86d55dc9e62fe82a2eb2c7508a42d4763f8ef0ff13abe137c83.webp)

In this tutorial, we learnt how to make minor modifications to an existing environment without affecting the original code.

It is important to note, however, that while the changes to be made may be small, they may not always work on the first try,
as there may be deeper dependencies on the original assets in the environment being modified.
In these cases, it is advisable to analyze the code of the available examples in detail in order to make an appropriate adjustment.