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robopianist-rl

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RL code for training piano-playing policies for RoboPianist.

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distributions.py
LICENSE
networks.py
README.md
replay.py
requirements.txt
run.sh
sac.py
specs.py
train.py

# Installation Guide

1. Get the code

git clone https://github.com/kevinzakka/robopianist-rl

Downloads the entire project code from GitHub to your computer.

cd robopianist-rl

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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# robopianist-rl

Reinforcement learning code for [RoboPianist](https://github.com/google-research/robopianist).

## Installation

Note: Make sure you are using the same conda environment you created for RoboPianist (see [here](https://github.com/google-research/robopianist/blob/main/README.md#installation)).

1. Install [JAX](https://github.com/google/jax#installation)
2. Run `pip install -r requirements.txt`

## Usage

We provide an example bash script to train a policy to play Twinkle Twinkle Little Star with the task parameters used in the paper.

```bash
bash run.sh
```

To look at all the possible command-line flags, run:

```bash
python train.py --help
```

## Citation

If you use this code, please cite our paper:

```bibtex
@article{zakka2023robopianist,
  author = {Zakka, Kevin and Smith, Laura and Gileadi, Nimrod and Howell, Taylor and Peng, Xue Bin and Singh, Sumeet and Tassa, Yuval and Florence, Pete and Zeng, Andy and Abbeel, Pieter},
  title = {{RoboPianist: A Benchmark for High-Dimensional Robot Control}},
  journal = {arXiv preprint arXiv:2304.04150},
  year = {2023},
}
```