Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation

Understand

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.