MARF: Cooperative Multi-Agent Path Finding with Reinforcement Learning and Frenet Lattice in Dynamic Environments

Multi-agent path finding (MAPF) in dynamic and complex environments is a highly challenging task. Recent research has focused on the scalability of agent numbers or the complexity of the environment. Usually, they disregard the agents' physical constraints or use a differential-driven model. However, this approach fails to adequately capture the kinematic and dynamic constraints of real-world vehicles, particularly those equipped with Ackermann steering. This paper presents a novel algorithm named MARF that combines multi-agent reinforcement learning (MARL) with a Frenet lattice planner. The MARL foundation endows the algorithm with enhanced generalization capabilities while preserving computational efficiency. By incorporating Frenet lattice trajectories into the action space of the MARL framework, agents are capable of generating smooth and feasible trajectories that respect the kinematic and dynamic constraints. In addition, we adopt a centralized training and decentralized execution (CTDE) framework, where a network of shared value functions enables efficient cooperation among agents during decision-making. Simulation results and real-world experiments in different scenarios demonstrate that our method achieves superior performance in terms of success rate, average speed, extra distance of trajectory, and computing time.