HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909118407639040 |
|---|---|
| author | Tang, Huijie Berto, Federico Ma, Zihan Hua, Chuanbo Ahn, Kyuree Park, Jinkyoo |
| author_facet | Tang, Huijie Berto, Federico Ma, Zihan Hua, Chuanbo Ahn, Kyuree Park, Jinkyoo |
| contents | Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. Traditional algorithms often fall short in scalability, especially in intricate scenarios. Reinforcement Learning (RL) has shown potential to address the intricacies of MAPF; however, it has also been shown to struggle with scalability, demanding intricate implementation, lengthy training, and often exhibiting unstable convergence, limiting its practical application. In this paper, we introduce Heuristics-Informed Multi-Agent Pathfinding (HiMAP), a novel scalable approach that employs imitation learning with heuristic guidance in a decentralized manner. We train on small-scale instances using a heuristic policy as a teacher that maps each single agent observation information to an action probability distribution. During pathfinding, we adopt several inference techniques to improve performance. With a simple training scheme and implementation, HiMAP demonstrates competitive results in terms of success rate and scalability in the field of imitation-learning-only MAPF, showing the potential of imitation-learning-only MAPF equipped with inference techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding Tang, Huijie Berto, Federico Ma, Zihan Hua, Chuanbo Ahn, Kyuree Park, Jinkyoo Multiagent Systems Artificial Intelligence Machine Learning Robotics Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. Traditional algorithms often fall short in scalability, especially in intricate scenarios. Reinforcement Learning (RL) has shown potential to address the intricacies of MAPF; however, it has also been shown to struggle with scalability, demanding intricate implementation, lengthy training, and often exhibiting unstable convergence, limiting its practical application. In this paper, we introduce Heuristics-Informed Multi-Agent Pathfinding (HiMAP), a novel scalable approach that employs imitation learning with heuristic guidance in a decentralized manner. We train on small-scale instances using a heuristic policy as a teacher that maps each single agent observation information to an action probability distribution. During pathfinding, we adopt several inference techniques to improve performance. With a simple training scheme and implementation, HiMAP demonstrates competitive results in terms of success rate and scalability in the field of imitation-learning-only MAPF, showing the potential of imitation-learning-only MAPF equipped with inference techniques. |
| title | HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding |
| topic | Multiagent Systems Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2402.15546 |