Summary of Breaking the Curse Of Multiagency in Robust Multi-agent Reinforcement Learning, by Laixi Shi et al.
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
by Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA); 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 distributionally robust Markov games (RMGs) aim to enhance robustness in multi-agent reinforcement learning (MARL) by optimizing worst-case performance when game dynamics shift within a prescribed uncertainty set. The authors explore this under-explored area, focusing on reasonable problem formulation and sample-efficient algorithms. They introduce a novel class of RMGs inspired by behavioral economics, where each agent’s uncertainty set is shaped by both the environment and other agents’ behavior. The paper establishes the well-posedness of these RMGs by proving the existence of robust Nash equilibria and coarse correlated equilibria. A sample-efficient algorithm is also introduced, breaking the curse of multiagency for RMGs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-agent reinforcement learning (MARL) algorithms are great at playing games with other AI systems or humans. However, they can struggle when the game rules change suddenly. To help with this, researchers have proposed a new type of game called distributionally robust Markov games (RMGs). These RMGs try to be good at all possible versions of the game, not just one specific version. The authors of this paper explore how to make RMGs work well and efficiently learn from experience. |
Keywords
* Artificial intelligence * Reinforcement learning
