Heterogeneous Multi-Robot Cooperation With Asynchronous Multi-Agent Reinforcement Learning

Abstract

Multi-robot systems (MRSs) are becoming increasingly important in various domains. However, effective communication and coordination among multiple robots remain significant challenges. In this letter, we introduce a novel architecture for multi-robot decision-making and control based on multi-agent reinforcement learning (MARL). Our architecture can accommodate heterogeneous robots operating asynchronously in different scenarios. We propose an improved practical Q-value mixing network (Qrainbow), which builds on value-decomposition networks and applies the multi-head attention mixer of Qatten and effective components from Rainbow, such as double network, dueling network, and prioritized experience replay. To migrate the algorithm to MRS, we fuse macro-action into Qrainbow and make a slight change to the process of calculating the loss function, enabling Qrainbow to work in asynchronous scenarios. We evaluate our architecture in both the benchmark environment for MARL and a multi-robot environment with varying layouts. In terms of convergence speed and final result, Qrainbow outperforms other state-of-the-art MARL algorithms. Additionally, our architecture achieves superior performance in reducing time costs and avoiding collisions between robots in homogeneous and heterogeneous multi-robot cooperation tasks.