Summary of Sample-efficient Robust Multi-agent Reinforcement Learning in the Face Of Environmental Uncertainty, by Laixi Shi et al.
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
by Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper proposes a novel approach to learning robust policies in multi-agent reinforcement learning (RL) environments. It focuses on distributionally robust Markov games (RMGs), where each agent aims to learn a policy that maximizes its own worst-case performance when the environment deviates within a prescribed uncertainty set. The authors introduce a sample-efficient model-based algorithm called DRNVI, which provides finite-sample complexity guarantees for learning robust variants of various game-theoretic equilibria. They also establish an information-theoretic lower bound for solving RMGs, confirming the near-optimal sample complexity of DRNVI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching machines to make good decisions in games where there are many players and unknown factors. Right now, these machines don’t do very well when things change unexpectedly. The researchers want to improve this by developing a new way for the machines to learn from mistakes. They call it “distributionally robust Markov games” and test it on different scenarios. |
Keywords
» Artificial intelligence » Reinforcement learning
