Summary of Mitigating Relative Over-generalization in Multi-agent Reinforcement Learning, by Ting Zhu et al.
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
by Ting Zhu, Yue Jin, Jeremie Houssineau, Giovanni Montana
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 MaxMax Q-Learning (MMQ) algorithm addresses the issue of relative over-generalization (RO) in decentralized multi-agent reinforcement learning, where individual agents learn to optimize their own actions without considering the collective impact. MMQ employs an iterative process that samples and evaluates potential next states, selecting those with maximal Q-values for learning. This approach refines approximations of ideal state transitions, aligning more closely with the optimal joint policy of collaborating agents. Theoretical analysis supports MMQ’s potential to improve coordination in cooperative tasks, and empirical evaluations across various environments demonstrate its effectiveness in outperforming existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decentralized multi-agent reinforcement learning lets agents learn alone, but this can cause problems when working together. A team might choose actions that are good for each individual, but bad for the group as a whole. To fix this, researchers introduced MaxMax Q-Learning (MMQ), which helps agents learn to work together more effectively. MMQ works by imagining and evaluating different future scenarios, choosing the ones that seem most promising. This helps agents refine their understanding of how they can best work together. The new approach is tested in various situations where it outperforms other methods. |
Keywords
* Artificial intelligence * Generalization * Reinforcement learning
