Multi-Agent Reinforcement Learning for Human-Robot Collaboration

Human-Robot Collaboration (HRC) has emerged as a critical area in intelligent systems, enabling robots and humans to work together seamlessly in complex environments. Recent advances in Reinforcement Learning (RL) have provided robust frameworks for adaptive decision-making, yet traditional single-agent RL often struggles to manage dynamic and uncertain multi-participant interactions. Multi-Agent Reinforcement Learning (MARL) addresses this limitation by allowing multiple agents—both human and robotic—to learn cooperative and competitive strategies simultaneously. This paper explores the integration of MARL in HRC, focusing on policy optimization, communication protocols, and shared reward mechanisms. The proposed framework leverages deep RL techniques to enhance adaptability, while considering factors such as safety, transparency, and scalability. Experimental simulations demonstrate that MARL can significantly improve task efficiency, coordination, and trust between humans and robots in collaborative settings. Furthermore, the study highlights the importance of explainable AI (XAI) in ensuring human operators can interpret agent decisions, fostering higher acceptance in real-world deployments. Overall, the findings suggest that MARL offers a promising direction for advancing HRC by bridging the gap between autonomous learning and human-centered collaboration.

کلیدواژه ها:

Multi-Agent Reinforcement Learning ، Human-Robot Collaboration ، Deep Reinforcement Learning ، Cooperative Strategies ، Policy Optimization ، Explainable AI