Achieving the Minimax Optimal Sample Complexity of Offline Reinforcement
Learning: A DRO-Based Approach

Achieving the Minimax Optimal Sample Complexity of Offline Reinforcement Learning: A DRO-Based Approach

22 May 2023

Jinjun Xiong

Papers citing "Achieving the Minimax Optimal Sample Complexity of Offline Reinforcement Learning: A DRO-Based Approach"

7 / 7 papers shown

Title
Adversarial Model for Offline Reinforcement Learning M. Bhardwaj Tengyang Xie Byron Boots Nan Jiang Ching-An Cheng AAML OffRL 27 25 0 21 Feb 2023
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage Masatoshi Uehara Wen Sun OffRL 96 144 0 13 Jul 2021
COMBO: Conservative Offline Model-Based Policy Optimization Tianhe Yu Aviral Kumar Rafael Rafailov Aravind Rajeswaran Sergey Levine Chelsea Finn OffRL 219 413 0 16 Feb 2021
On Linear Convergence of Policy Gradient Methods for Finite MDPs Jalaj Bhandari Daniel Russo 55 59 0 21 Jul 2020
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL Seyed Kamyar Seyed Ghasemipour Dale Schuurmans S. Gu OffRL 209 119 0 21 Jul 2020
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine Aviral Kumar George Tucker Justin Fu OffRL GP 334 1,951 0 04 May 2020
Deep Reinforcement Learning for Autonomous Driving: A Survey B. R. Kiran Ibrahim Sobh V. Talpaert Patrick Mannion A. A. Sallab S. Yogamani P. Pérez 165 1,630 0 02 Feb 2020