Safe and Efficient Multi-Agent Collision Avoidance With Physics-Informed Reinforcement Learning

Abstract

Reinforcement learning (RL) has shown great promise in addressing multi-agent collision avoidance challenges. However, existing RL-based methods often suffer from low training efficiency and poor action safety. To tackle these issues, we introduce a physics-informed reinforcement learning framework equipped with two modules: a Potential Field (PF) module and a Multi-Agent Multi-Level Safety (MAMLS) module. The PF module uses the Artificial Potential Field method to compute a regularization loss, adaptively integrating it into the critic's loss to enhance training efficiency. The MAMLS module formulates action safety as a constrained optimization problem, deriving safe actions by solving this optimization. Furthermore, to better address the characteristics of multi-agent collision avoidance tasks, multi-agent multi-level constraints are introduced. The results of simulations and real-world experiments showed that our physics-informed framework offers a significant improvement in terms of both the efficiency of training and safety-related metrics over advanced baseline methods.