We consider the adversarial Markov Decision Process (MDP) problem, where the rewards for the MDP can be adversarially chosen, and the transition function can be either known or unknown. In both settings, Follow-the-PerturbedLeader (FPL) based algorithms have been proposed in previous literature. However, the established regret bounds for FPL based algorithms are worse than algorithms based on mirrordescent. We improve the analysis of FPL based algorithms in both settings, matching the current best regret bounds using faster and simpler algorithms.
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
HSE University（高等经济大学）；Centre de Mathématiques Appliquées（应用数学中心）；CNRS（法国国家科学研究中心）；École polytechnique（巴黎综合理工学院）；Institut Polytechnique de Paris（巴黎理工学院）；Université Paris-Saclay（巴黎萨克雷大学）；Laboratoire de mathématiques d’Orsay（奥尔赛数学实验室）；Steklov Mathematical Institute of Russian Academy of Sciences（俄罗斯科学院斯捷克洛夫数学研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（赫尔曼·米勒斯托德工程与系统工程学院，佐治亚理工学院）；Qiuzhen College, Tsinghua University（清华学院，清华大学）；Yau Mathematical Sciences Center & Department of Mathematical Sciences, Tsinghua University（姚数学科学中心及数学科学系，清华大学）；Beijing Institute of Mathematical Sciences and Applications（北京数学科学与应用研究所）
