  In recent advancements in Multi-agent Reinforcement Learning (MARL), its
application has extended to various safety-critical scenarios. However, most
methods focus on online learning, which presents substantial risks when
deployed in real-world settings. Addressing this challenge, we introduce an
innovative framework integrating diffusion models within the MARL paradigm.
This approach notably enhances the safety of actions taken by multiple agents
through risk mitigation while modeling coordinated action. Our framework is
grounded in the Centralized Training with Decentralized Execution (CTDE)
architecture, augmented by a Diffusion Model for prediction trajectory
generation. Additionally, we incorporate a specialized algorithm to further
ensure operational safety. We evaluate our model against baselines on the DSRL
benchmark. Experiment results demonstrate that our model not only adheres to
stringent safety constraints but also achieves superior performance compared to
existing methodologies. This underscores the potential of our approach in
advancing the safety and efficacy of MARL in real-world applications.
