LC-MAP: A Learnable Communication Module for Multi-Agent Pathfinding
Researchers have introduced Local Communication for Multi-agent Pathfinding (LC-MAP), a learnable communication module designed to enhance cooperation between agents in large-scale multi-agent pathfinding (MAPF) problems. MAPF is a key abstraction for multi-robot trajectory planning, where homogeneous agents move simultaneously in a shared environment. Solving MAPF optimally is NP-hard, but scalable solvers are critical for logistics and search-and-rescue. The team frames MAPF as a Dec-POMDP from a single agent perspective, using reinforcement or imitation learning. LC-MAP adds efficient feature sharing between agents, improving coordination. The paper is available on arXiv under ID 2605.07637.
Key facts
- LC-MAP introduces a learnable communication module for multi-agent pathfinding.
- MAPF is used for multi-robot trajectory planning in shared environments.
- Optimal MAPF solving is NP-hard.
- Scalable MAPF solvers are critical for logistics and search-and-rescue.
- The approach frames MAPF as a Dec-POMDP from a single agent perspective.
- LC-MAP uses reinforcement or imitation learning.
- The module enables efficient feature sharing between agents.
- The paper is on arXiv with ID 2605.07637.
Entities
Institutions
- arXiv