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[AAAI'25 Oral] Are Expressive Models Truly Necessary for Offline RL?

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# Recursive Skip-Step Planning (RSP)

This repository is the official implementation of Recursive Skip-step Planning in JAX, attached with the paper entitled [Are Expressive Models Truly Necessary for Offline RL?](https://openreview.net/forum?id=19KvVggjVr) (AAAI 2025).

## Getting Started

### Prerequisites

1. [Install JAX](https://github.com/google/jax#installation)

2. [Install W&B](https://github.com/wandb/wandb) and log in to your account to view metrics

3. Install the required dependencies:

```bash
pip install -r requirements.txt
```

### Running experiment sets

To reproduce the D4RL benchmark results:

```bash
# Adroit
python exp_launcher.py include=experiment_conf/adroit.yaml
# AntMaze
python exp_launcher.py include=experiment_conf/antmaze.yaml
# MuJoCo
python exp_launcher.py include=experiment_conf/mujoco.yaml
# Franka Kitchen
python exp_launcher.py include=experiment_conf/kitchen.yaml
```

Metrics are uploaded to W&B. The final performance is also stored in the `metrics` folder. To consolidate into a markdown report, run the following command:

```bash
python view_metrics.py
```

This report will be saved in `report.md`.

**Note**: The experiment launcher will automatically allocate all idle GPUs on your machine and run experiments in parallel.

## License

Apache License 2.0

## Citation
```
@inproceedings{
          wang-niu2024rsp,
          title={Are Expressive Models Truly Necessary for Offline {RL}?},
          author={Guan Wang and Haoyi Niu and Jianxiong Li and Li Jiang and Jianming HU and Xianyuan Zhan},
          booktitle={The 39th Annual AAAI Conference on Artificial Intelligence (AAAI)},
          year={2025}
}
```

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