Multi-agent Reinforcement Learning for Robotics: Examining multi-agent reinforcement learning algorithms for training teams of robots to collaborate and achieve common goals

Abstract

Multi-agent reinforcement learning (MARL) has emerged as a promising approach for training teams of robots to collaborate and achieve common goals. This paper provides an overview of MARL algorithms and their applications in robotics, focusing on the challenges and opportunities they present. We first discuss the basics of reinforcement learning (RL) and its extension to the multi-agent setting. We then review state-of-the-art MARL algorithms and their use in robotics, highlighting key advancements and open research questions. Finally, we discuss future directions for research in this area, emphasizing the potential impact of MARL on the field of robotics.