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20222026
most citedLearning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach

2 citations · 8 across the 12 of their papers we have counts for

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cs.LG2026

Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents

Hanzhen Wang, Boxiang Lyu, Zhongxiang Dai +3

Risk-sensitive reinforcement learning (RL) addresses decision-making under uncertainty and adverse outcomes. Learning risk-sensitive policies from fixed datasets is important when…

cs.LG2026

Model-based Offline RL via Robust Value-Aware Model Learning with Implicitly Differentiable Adaptive Weighting

Zhongjian Qiao, Jiafei Lyu, Boxiang Lyu +3

Model-based offline reinforcement learning (RL) aims to enhance offline RL with a dynamics model that facilitates policy exploration. However, \textit{model exploitation} could occ…