Multi Agent Pathfinding

Multi-agent pathfinding (MAPF) tackles the complex problem of finding collision-free paths for multiple agents navigating a shared environment, aiming for efficient and optimal solutions. Current research emphasizes developing scalable algorithms, including those based on reinforcement learning (with various communication strategies and reward shaping techniques), imitation learning, and hierarchical planning approaches that decompose the problem into smaller, more manageable subproblems. These advancements are crucial for optimizing various applications, such as warehouse automation, robotics, and traffic management, where efficient coordination of multiple agents is paramount.