Nearly Minimax Optimal Regret for Learning Infinite-horizon
Average-reward MDPs with Linear Function Approximation

Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation

15 February 2021

Quanquan Gu

Papers citing "Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation"

7 / 7 papers shown

Title
Predictive Control and Regret Analysis of Non-Stationary MDP with Look-ahead Information Ziyi Zhang Yorie Nakahira Guannan Qu 36 1 0 13 Sep 2024
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space Jonatha Anselmi B. Gaujal Louis-Sébastien Rebuffi 27 2 0 21 Feb 2023
Learning Stochastic Shortest Path with Linear Function Approximation Steffen Czolbe Jiafan He Adrian Dalca Quanquan Gu 44 30 0 25 Oct 2021
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes Chonghua Liao Jiafan He Quanquan Gu 27 17 0 19 Oct 2021
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs Jiafan He Dongruo Zhou Quanquan Gu 95 24 0 17 Feb 2021
Optimism in Reinforcement Learning with Generalized Linear Function Approximation Yining Wang Ruosong Wang S. Du A. Krishnamurthy 135 135 0 09 Dec 2019
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes Chen-Yu Wei Mehdi Jafarnia-Jahromi Haipeng Luo Hiteshi Sharma R. Jain 107 100 0 15 Oct 2019