Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation

• URL: http://arxiv.org/abs/2003.09540v1
• Date: Sat, 21 Mar 2020 00:43:54 GMT
• Title: Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation
• Authors: Guohui Ding, Joewie J. Koh, Kelly Merckaert, Bram Vanderborght, Marco M. Nicotra, Christoffer Heckman, Alessandro Roncone, Lijun Chen
• Abstract summary: 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) and game-theoretic RL (GT-RL) 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.
• Score: 53.262360083572005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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