LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929692831907840 |
|---|---|
| author | Wang, Yutong Duhan, Tanishq Li, Jiaoyang Sartoretti, Guillaume |
| author_facet | Wang, Yutong Duhan, Tanishq Li, Jiaoyang Sartoretti, Guillaume |
| contents | Multi-Agent Path Finding (MAPF) is a critical component of logistics and warehouse management, which focuses on planning collision-free paths for a team of robots in a known environment. Recent work introduced a novel MAPF approach, LNS2, which proposed to repair a quickly obtained set of infeasible paths via iterative replanning, by relying on a fast, yet lower-quality, prioritized planning (PP) algorithm. At the same time, there has been a recent push for Multi-Agent Reinforcement Learning (MARL) based MAPF algorithms, which exhibit improved cooperation over such PP algorithms, although inevitably remaining slower. In this paper, we introduce a new MAPF algorithm, LNS2+RL, which combines the distinct yet complementary characteristics of LNS2 and MARL to effectively balance their individual limitations and get the best from both worlds. During early iterations, LNS2+RL relies on MARL for low-level replanning, which we show eliminates collisions much more than a PP algorithm. There, our MARL-based planner allows agents to reason about past and future information to gradually learn cooperative decision-making through a finely designed curriculum learning. At later stages of planning, LNS2+RL adaptively switches to PP algorithm to quickly resolve the remaining collisions, naturally trading off solution quality (number of collisions in the solution) and computational efficiency. Our comprehensive experiments on high-agent-density tasks across various team sizes, world sizes, and map structures consistently demonstrate the superior performance of LNS2+RL compared to many MAPF algorithms, including LNS2, LaCAM, EECBS, and SCRIMP. In maps with complex structures, the advantages of LNS2+RL are particularly pronounced, with LNS2+RL achieving a success rate of over 50% in nearly half of the tested tasks, while that of LaCAM, EECBS and SCRIMP falls to 0%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17794 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding Wang, Yutong Duhan, Tanishq Li, Jiaoyang Sartoretti, Guillaume Robotics Multi-Agent Path Finding (MAPF) is a critical component of logistics and warehouse management, which focuses on planning collision-free paths for a team of robots in a known environment. Recent work introduced a novel MAPF approach, LNS2, which proposed to repair a quickly obtained set of infeasible paths via iterative replanning, by relying on a fast, yet lower-quality, prioritized planning (PP) algorithm. At the same time, there has been a recent push for Multi-Agent Reinforcement Learning (MARL) based MAPF algorithms, which exhibit improved cooperation over such PP algorithms, although inevitably remaining slower. In this paper, we introduce a new MAPF algorithm, LNS2+RL, which combines the distinct yet complementary characteristics of LNS2 and MARL to effectively balance their individual limitations and get the best from both worlds. During early iterations, LNS2+RL relies on MARL for low-level replanning, which we show eliminates collisions much more than a PP algorithm. There, our MARL-based planner allows agents to reason about past and future information to gradually learn cooperative decision-making through a finely designed curriculum learning. At later stages of planning, LNS2+RL adaptively switches to PP algorithm to quickly resolve the remaining collisions, naturally trading off solution quality (number of collisions in the solution) and computational efficiency. Our comprehensive experiments on high-agent-density tasks across various team sizes, world sizes, and map structures consistently demonstrate the superior performance of LNS2+RL compared to many MAPF algorithms, including LNS2, LaCAM, EECBS, and SCRIMP. In maps with complex structures, the advantages of LNS2+RL are particularly pronounced, with LNS2+RL achieving a success rate of over 50% in nearly half of the tested tasks, while that of LaCAM, EECBS and SCRIMP falls to 0%. |
| title | LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.17794 |