We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems.
@article{arxiv.2003.09540,
title = {Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation},
author = {Guohui Ding and Joewie J. Koh and Kelly Merckaert and Bram Vanderborght and Marco M. Nicotra and Christoffer Heckman and Alessandro Roncone and Lijun Chen},
journal= {arXiv preprint arXiv:2003.09540},
year = {2021}
}