Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning
<|reference_start|>Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning: We study risk-sensitive multi-agent reinforcement learning under general-sum Markov games, where agents optimize the entropic risk measure of rewards with possibly diverse risk preferences. We show that using the regret naively adapted from existing literature as a performance metric could induce policies with equilibrium bias that favor the most risk-sensitive agents and overlook the other agents. To address such deficiency of the naive regret, we propose a novel notion of regret, which we call risk-balanced regret, and show through a lower bound that it overcomes the issue of equilibrium bias. Furthermore, we develop a self-play algorithm for learning Nash, correlated, and coarse correlated equilibria in risk-sensitive Markov games. We prove that the proposed algorithm attains near-optimal regret guarantees with respect to the risk-balanced regret.<|reference_end|>
@article{fei2024taming,
    title={Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement
  Learning},
    author={Yingjie Fei, Ruitu Xu},
    journal={arXiv preprint arXiv:2405.02724},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02724},
    primaryClass={cs.LG cs.GT}
}