A Review of Developments in Reinforcement Learning for Multi-robot Systems

Abstract

Reinforcement learning( RL) is an effective mean for multi-robot systems to adapt to complex and uncertain environments. It is considered as one of the key technologies in designing intelligent systems. Based on the basic ideas and theoretical framework of reinforcement learning,main challenges such as partial observation,computational complexity and convergence were focused. The state of the art and difficulties were summarized in terms of communication issues, cooperative learning,credit assignment and interpretability. Applications in path planning and obstacle avoidance,unmanned aerial vehicles, robot football, the multi-robot pursuit-evasion problem, etc., were introduced. Finally,the frontier technologies such as qualitative RL,fractal RL and information fusion RL,were discussed to track its future development.