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20172024
most citedDynamic Regret of Policy Optimization in Non-stationary Environments

11 citations · 38 across the 9 of their papers we have counts for

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cs.LG2024

Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning

Yingjie Fei, Ruitu Xu

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 pre…

cs.LG2022

Cascaded Gaps: Towards Gap-Dependent Regret for Risk-Sensitive Reinforcement Learning

Yingjie Fei, Ruitu Xu

In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement learning based on the entropic risk measure. We propose a novel definition of sub-optimalit…

cs.LG2021★ 9 cited

Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning

Yingjie Fei, Zhuoran Yang, Yudong Chen +1

We study risk-sensitive reinforcement learning (RL) based on the entropic risk measure. Although existing works have established non-asymptotic regret guarantees for this problem,…

cs.LG2020★ 11 cited

Dynamic Regret of Policy Optimization in Non-stationary Environments

Yingjie Fei, Zhuoran Yang, Zhaoran Wang +1

We consider reinforcement learning (RL) in episodic MDPs with adversarial full-information reward feedback and unknown fixed transition kernels. We propose two model-free policy op…

cs.LG2020★ 9 cited

Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

Yingjie Fei, Zhuoran Yang, Yudong Chen +2

We study risk-sensitive reinforcement learning in episodic Markov decision processes with unknown transition kernels, where the goal is to optimize the total reward under the risk…