Subdimensional Expansion Using Attention-Based Learning For Multi-Agent Path Finding

• URL: http://arxiv.org/abs/2109.14695v1
• Date: Wed, 29 Sep 2021 20:01:04 GMT
• Title: Subdimensional Expansion Using Attention-Based Learning For Multi-Agent Path Finding
• Authors: Lakshay Virmani, Zhongqiang Ren, Sivakumar Rathinam and Howie Choset
• Abstract summary: Multi-Agent Path Finding (MAPF) finds conflict-free paths for multiple agents from their respective start to goal locations. We develop a novel multi-agent planner called LM* by integrating this learning-based single-agent planner with M*. Our results show that for both "seen" and "unseen" maps, in comparison with M*, LM* has fewer conflicts to be resolved and thus, runs faster and enjoys higher success rates.
• Score: 9.2127262112464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Agent Path Finding (MAPF) finds conflict-free paths for multiple agents from their respective start to goal locations. MAPF is challenging as the joint configuration space grows exponentially with respect to the number of agents. Among MAPF planners, search-based methods, such as CBS and M*, effectively bypass the curse of dimensionality by employing a dynamically-coupled strategy: agents are planned in a fully decoupled manner at first, where potential conflicts between agents are ignored; and then agents either follow their individual plans or are coupled together for planning to resolve the conflicts between them. In general, the number of conflicts to be resolved decides the run time of these planners and most of the existing work focuses on how to efficiently resolve these conflicts. In this work, we take a different view and aim to reduce the number of conflicts (and thus improve the overall search efficiency) by improving each agent's individual plan. By leveraging a Visual Transformer, we develop a learning-based single-agent planner, which plans for a single agent while paying attention to both the structure of the map and other agents with whom conflicts may happen. We then develop a novel multi-agent planner called LM* by integrating this learning-based single-agent planner with M*. Our results show that for both "seen" and "unseen" maps, in comparison with M*, LM* has fewer conflicts to be resolved and thus, runs faster and enjoys higher success rates. We empirically show that MAPF solutions computed by LM* are near-optimal. Our code is available at https://github.com/lakshayvirmani/learning-assisted-mstar .

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