Refined Regret for Adversarial MDPs with Linear Function Approximation

Refined Regret for Adversarial MDPs with Linear Function Approximation

30 January 2023

Papers citing "Refined Regret for Adversarial MDPs with Linear Function Approximation"

12 / 12 papers shown

Title
Enhancing PPO with Trajectory-Aware Hybrid Policies Qisai Liu Zhanhong Jiang Hsin-Jung Yang Mahsa Khosravi Joshua R. Waite S. Sarkar 46 0 0 21 Feb 2025
Imitation Learning in Discounted Linear MDPs without exploration assumptions Luca Viano Stratis Skoulakis V. Cevher 30 3 0 03 May 2024
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation Yan Dai Qiwen Cui S. S. Du 44 1 0 11 Feb 2024
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback Canzhe Zhao Ruofeng Yang Baoxiang Wang Xuezhou Zhang Shuai Li 27 2 0 14 Nov 2023
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback Haolin Liu Chen-Yu Wei Julian Zimmert 22 6 0 17 Oct 2023
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits Haolin Liu Chen-Yu Wei Julian Zimmert 30 9 0 02 Sep 2023
Rate-Optimal Policy Optimization for Linear Markov Decision Processes Uri Sherman Alon Cohen Tomer Koren Yishay Mansour 38 7 0 28 Aug 2023
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes Han Zhong Tong Zhang 32 26 0 15 May 2023
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback Tal Lancewicki Aviv A. Rosenberg Dmitry Sotnikov 29 3 0 13 May 2023
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization Fang-yuan Kong Xiangcheng Zhang Baoxiang Wang Shuai Li 26 12 0 14 Feb 2023
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation Uri Sherman Tomer Koren Yishay Mansour 32 12 0 30 Jan 2023
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs Jiafan He Dongruo Zhou Quanquan Gu 95 23 0 17 Feb 2021