The Benefits of Being Distributional: Small-Loss Bounds for
Reinforcement Learning

The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning

25 May 2023

Papers citing "The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning"

10 / 10 papers shown

Title
Diffusing States and Matching Scores: A New Framework for Imitation Learning Runzhe Wu Yiding Chen Gokul Swamy Kianté Brantley Wen Sun DiffM 37 3 0 17 Oct 2024
The Central Role of the Loss Function in Reinforcement Learning Kaiwen Wang Nathan Kallus Wen Sun OffRL 41 7 0 19 Sep 2024
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation Taehyun Cho Seung Han Kyungjae Lee Seokhun Ju Dohyeong Kim Jungwoo Lee 59 0 0 31 Jul 2024
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation Yu Chen Xiangcheng Zhang Siwei Wang Longbo Huang 29 3 0 28 Feb 2024
Settling the Sample Complexity of Online Reinforcement Learning Zihan Zhang Yuxin Chen Jason D. Lee S. Du OffRL 90 21 0 25 Jul 2023
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach Andrew Wagenmaker Yifang Chen Max Simchowitz S. Du Kevin G. Jamieson 71 36 0 07 Dec 2021
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage Masatoshi Uehara Wen Sun OffRL 91 144 0 13 Jul 2021
Conservative Offline Distributional Reinforcement Learning Yecheng Jason Ma Dinesh Jayaraman Osbert Bastani OffRL 65 78 0 12 Jul 2021
Model-free Representation Learning and Exploration in Low-rank MDPs Aditya Modi Jinglin Chen A. Krishnamurthy Nan Jiang Alekh Agarwal OffRL 98 78 0 14 Feb 2021
Reward-Free Exploration for Reinforcement Learning Chi Jin A. Krishnamurthy Max Simchowitz Tiancheng Yu OffRL 104 194 0 07 Feb 2020