Summary of Taming Equilibrium Bias in Risk-sensitive Multi-agent Reinforcement Learning, by Yingjie Fei et al.
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning
by Yingjie Fei, Ruitu Xu
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 A novel approach is proposed to address limitations in existing methods for multi-agent reinforcement learning under general-sum Markov games, where agents optimize entropic risk measures of rewards with diverse risk preferences. The authors show that naive regret metrics can induce policies favoring the most risk-sensitive agents, leading to equilibrium bias. To overcome this issue, a novel risk-balanced regret metric is introduced, along with a self-play algorithm for learning Nash, correlated, and coarse correlated equilibria in risk-sensitive Markov games. The proposed approach attains near-optimal regret guarantees with respect to the risk-balanced regret. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop new methods to help multiple agents learn together in complex situations. They show that current approaches can lead to biases, where some agents get more attention than others. To fix this problem, they introduce a new way of measuring how well an agent does (called risk-balanced regret) and design an algorithm for learning different types of equilibriums. This work is important because it helps us understand how multiple agents can learn together in complex environments. |
Keywords
» Artificial intelligence » Attention » Reinforcement learning
