Cube Stacking RL

This project aims to develop a reinforcement learning (RL) agent that can stack cubes in a simulated environment.

Prerequisites

Before getting started, make sure you have the following installed:

Python 3.x
RL libraries (e.g., OpenAI Gym, Stable Baselines3)

Installation

Clone the repository:

https://github.com/salvingeorge/cube_stacking_rl.git

Navigate to the project directory:
```
cd cube_stacking_rl
```
Create a virtual environment (optional but recommended):
```
python3 -m venv venv
source venv/bin/activate
```
Install the required dependencies:
```
pip install -r requirements.txt
```

Usage

download the dataset

python -m mani_skill2.utils.download_demo "StackCube-v0"

Convert the dataset into trajectories:

python -m mani_skill2.trajectory.replay_trajectory --traj-path demos/v0/rigid_body/StackCube-v0/trajectory.h5 --save-traj -o state -c pd_ee_delta_pose --num-procs 8

Train the agent:

python state_imitation_learning_stackcube.py --demos=demos/v0/rigid_body/StackCube-v0/trajectory.state.pd_ee_delta_pose.h5

This will load the train the model and evaluate its performance.

When prompted, enter your wandb details

Contributing

If you would like to contribute to this project, follow these steps:

Fork the repository on GitHub.

Clone your forked repository:

git clone https://github.com/your-username/cube_stacking_rl.git

Create a new branch for your changes:

git checkout -b feature/your-feature-name

Make your changes and commit them:

git commit -m "Add your commit message here"

Push your changes to your forked repository:
```
git push origin feature/your-feature-name
```
Open a pull request on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for more details.