Summary of Cooperative Reward Shaping For Multi-agent Pathfinding, by Zhenyu Song et al.
Cooperative Reward Shaping for Multi-Agent Pathfinding
by Zhenyu Song, Ronghao Zheng, Senlin Zhang, Meiqin Liu
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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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 paper introduces a novel reward shaping technique for Multi-Agent Reinforcement Learning (MARL) to improve cooperation among agents in Multi-Agent Pathfinding (MAPF). The traditional MARL approach lacks global information, leading to reduced MAPF efficiency. To address this challenge, the authors propose Independent Q-Learning (IQL)-based reward shaping that evaluates the influence of one agent on its neighbors and integrates it into the reward function. This technique facilitates cooperation among agents while operating in a distributed manner. The proposed approach is evaluated through experiments across various scenarios with different scales and agent counts, outperforming state-of-the-art planners in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how we can make computers help each other find the best path when there are many things moving around. Right now, these computer systems don’t work well together because they don’t have all the information. The authors came up with a new way to make them work better by giving them more context about what’s happening around them. This makes it easier for them to find paths that avoid crashes and work efficiently. They tested this approach on different scenarios and found that it works better than other methods in some cases. |
Keywords
» Artificial intelligence » Reinforcement learning
