Summary of Multi-agent Target Assignment and Path Finding For Intelligent Warehouse: a Cooperative Multi-agent Deep Reinforcement Learning Perspective, by Qi Liu et al.
Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective
by Qi Liu, Jianqi Gao, Dongjie Zhu, Zhongjian Qiao, Pengbin Chen, Jingxiang Guo, Yanjie Li
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method tackles two crucial problems in intelligent warehouse management: multi-agent target assignment and path planning. By leveraging cooperative multi-agent deep reinforcement learning (RL), the study simultaneously solves these problems, a novel approach that considers the physical dynamics of agents. The experiment demonstrates the effectiveness of the method in various task settings, achieving reasonable target assignment and nearly shortest planned paths. In comparison to baselines, the proposed method exhibits improved time efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In intelligent warehouses, robots need to work together to assign targets and plan paths efficiently. A new approach uses deep learning to solve both problems at once. This helps robots move around obstacles and get to their targets quickly. The study shows that this method works well in different scenarios and is faster than previous methods. |
Keywords
* Artificial intelligence * Deep learning * Reinforcement learning
