Multi-Robot Warehouse Environment (RWARE)

A multi-agent environment for Reinforcement Learning

Please cite:

If you use this environment, consider citing

1. A comperative evaluation of MARL algorithms that includes this environment

@article{papoudakis2020comparative,
  title={Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms},
  author={Papoudakis, Georgios and Christianos, Filippos and Sch{\"a}fer, Lukas and Albrecht, Stefano V},
  journal={arXiv preprint arXiv:2006.07869},
  year={2020}
}

2. A method that achieves state-of-the-art performance in the robotic warehouse task

@inproceedings{christianos2020shared,
 author = {Christianos, Filippos and Sch\"{a}fer, Lukas and Albrecht, Stefano},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {10707--10717},
 publisher = {Curran Associates, Inc.},
 title = {Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning},
 url = {https://proceedings.neurips.cc/paper/2020/file/7967cc8e3ab559e68cc944c44b1cf3e8-Paper.pdf},
 volume = {33},
 year = {2020}
}

Installation:

git clone https://github.com/semitable/robotic-warehouse.git
cd robotic-warehouse
pip install -e .

Usage

To use in Python, import it and use it as a gym environment:

import robotic_warehouse
import gym

env = gym.make("rware-tiny-2ag-v1")
n_obs = env.reset()
n_obs, n_reward, n_done, info = env.step(n_action)

where n_* is a list of length len(agents)