  Safe Multi-agent reinforcement learning (safe MARL) has increasingly gained
attention in recent years, emphasizing the need for agents to not only optimize
the global return but also adhere to safety requirements through behavioral
constraints. Some recent work has integrated control theory with multi-agent
reinforcement learning to address the challenge of ensuring safety. However,
there have been only very limited applications of Model Predictive Control
(MPC) methods in this domain, primarily due to the complex and implicit
dynamics characteristic of multi-agent environments. To bridge this gap, we
propose a novel method called Deep Learning-Based Model Predictive Control for
Safe Multi-Agent Reinforcement Learning (DeepSafeMPC). The key insight of
DeepSafeMPC is leveraging a entralized deep learning model to well predict
environmental dynamics. Our method applies MARL principles to search for
optimal solutions. Through the employment of MPC, the actions of agents can be
restricted within safe states concurrently. We demonstrate the effectiveness of
our approach using the Safe Multi-agent MuJoCo environment, showcasing
significant advancements in addressing safety concerns in MARL.
