Name
Title | ||
|---|---|---|
Learning to Cooperate: Application of Deep Reinforcement Learning for Online AGV Path Finding |
Abstract | ||
|---|---|---|
Multi-agent path finding (MAPF), naturally exists in applications like picking-up and dropping-off parcels by automated guided vehicles (AGVs) in the warehouse. Existing algorithms, like conflict-based search (CBS), windowed hierarchical cooperative A* (WHCA), and other A* variants, are widely used to find the shortest paths in different manners. However, in real-world environments, MAPF cases are dynamically generated and need to be solved in real time. In this work, a decentralized multi-agent reinforcement learning (MARL) framework with multi-step ahead tree search (MATS) strategy is proposed to make efficient decisions. Through performing experiments on a 30*30 grid world and a real-world warehouse case, our proposed MARL policy is proved to be capable of: 1) scaling to a large number of agents in real-world environment with online response time within acceptable levels; 2) outperforming existing algorithms with shorter path length and solution time, as the number of agents increases.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.5555/3398761.3399080 | AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems
Auckland
New Zealand
May, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7518-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zhang | 1 | 0 | 0.34 |
| Qian Yu | 2 | 7 | 7.22 |
| Yichen Yao | 3 | 0 | 0.68 |
| Haoyuan Hu | 4 | 0 | 1.35 |
| Yinghui Xu | 5 | 172 | 20.23 |