# Train RL in Simulation

This guide explains how to use the `gym_hil` simulation environments as an alternative to real robots when working with the LeRobot framework for Human-In-the-Loop (HIL) reinforcement learning.

`gym_hil` is a package that provides Gymnasium-compatible simulation environments specifically designed for Human-In-the-Loop reinforcement learning. These environments allow you to:

- Train policies in simulation to test the RL stack before training on real robots

- Collect demonstrations in sim using external devices like gamepads or keyboards
- Perform human interventions during policy learning

Currently, the main environment is a Franka Panda robot simulation based on MuJoCo, with tasks like picking up a cube.


## Installation

First, install the `gym_hil` package within the LeRobot environment:

```bash
pip install -e ".[hilserl]"
```

## What do I need?

- A gamepad or keyboard to control the robot
- A Nvidia GPU



## Configuration

To use `gym_hil` with LeRobot, you need to create a configuration file. An example is provided [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/gym_hil_env.json). Key configuration sections include:

### Environment Type and Task

```json
{
    "type": "hil",
    "name": "franka_sim",
    "task": "PandaPickCubeGamepad-v0",
    "device": "cuda"
}
```

Available tasks:
- `PandaPickCubeBase-v0`: Basic environment
- `PandaPickCubeGamepad-v0`: With gamepad control
- `PandaPickCubeKeyboard-v0`: With keyboard control

### Gym Wrappers Configuration

```json
"wrapper": {
    "gripper_penalty": -0.02,
    "control_time_s": 15.0,
    "use_gripper": true,
    "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785],
    "end_effector_step_sizes": {
        "x": 0.025,
        "y": 0.025,
        "z": 0.025
    },
    "control_mode": "gamepad"
    }
```

Important parameters:
- `gripper_penalty`: Penalty for excessive gripper movement
- `use_gripper`: Whether to enable gripper control
- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
- `control_mode`: Set to `"gamepad"` to use a gamepad controller

## Running with HIL RL of LeRobot

### Basic Usage

To run the environment, set mode to null:

```python
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
```

### Recording a Dataset

To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:

```python
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
```

### Training a Policy

To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_gym_hil_env.json) and run the actor and learner servers:

```python
python lerobot/scripts/rl/actor.py --config_path path/to/train_gym_hil_env.json
```

In a different terminal, run the learner server:

```python
python lerobot/scripts/rl/learner.py --config_path path/to/train_gym_hil_env.json
```

The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots.

Congrats 🎉, you have finished this tutorial!

> [!TIP]
>  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

Paper citation:
```
@article{luo2024precise,
  title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
  author={Luo, Jianlan and Xu, Charles and Wu, Jeffrey and Levine, Sergey},
  journal={arXiv preprint arXiv:2410.21845},
  year={2024}
}
```
