Multi-Agent Path Finding with Prioritized Communication Learning
This addresses collision avoidance in multi-agent systems like automation warehouses, representing an incremental improvement over existing learning-based methods.
The paper tackles the problem of high collision rates and low success rates in decentralized multi-agent pathfinding by proposing PICO, a method that learns implicit planning priorities to form dynamic communication topologies, resulting in significantly better performance in success and collision rates compared to state-of-the-art learning-based planners.
Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world problems, e.g., automation warehouses. The learning-based, fully decentralized framework has been introduced to alleviate real-time problems and simultaneously pursue optimal planning policy. However, existing methods might generate significantly more vertex conflicts (or collisions), which lead to a low success rate or more makespan. In this paper, we propose a PrIoritized COmmunication learning method (PICO), which incorporates the \textit{implicit} planning priorities into the communication topology within the decentralized multi-agent reinforcement learning framework. Assembling with the classic coupled planners, the implicit priority learning module can be utilized to form the dynamic communication topology, which also builds an effective collision-avoiding mechanism. PICO performs significantly better in large-scale MAPF tasks in success rates and collision rates than state-of-the-art learning-based planners.