Multi-Agent Path Finding with Prioritized Communication Learning
| Autor: | Wenhao Li, Hongjun Chen, Bo Jin, Wenzhe Tan, Hongyuan Zha, Xiangfeng Wang |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Robotics Computer Science - Machine Learning Computer Science - Computer Science and Game Theory Computer Science - Multiagent Systems Robotics (cs.RO) Computer Science and Game Theory (cs.GT) Machine Learning (cs.LG) Multiagent Systems (cs.MA) |
| DOI: | 10.48550/arxiv.2202.03634 |
| Popis: | 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. Comment: 7 pages, 5 figures, 3 tables, ICRA 2022 Camera Ready |
| Databáze: | OpenAIRE |
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