On Lower Bounds for Regret in Reinforcement Learning

9 August 2016

Papers citing "On Lower Bounds for Regret in Reinforcement Learning"

45 / 45 papers shown

Title
Reinforcement Learning from Multi-level and Episodic Human Feedback Muhammad Qasim Elahi Somtochukwu Oguchienti Maheed H. Ahmed Mahsa Ghasemi OffRL 57 0 0 20 Apr 2025
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling Jasmine Bayrooti Carl Henrik Ek Amanda Prorok 86 0 0 07 Oct 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 72 0 0 31 Jul 2024
Cascading Reinforcement Learning Yihan Du R. Srikant Wei Chen 24 0 0 17 Jan 2024
Learning in POMDPs is Sample-Efficient with Hindsight Observability Jonathan Lee Alekh Agarwal Christoph Dann Tong Zhang 39 20 0 31 Jan 2023
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient Dylan J. Foster Noah Golowich Jian Qian Alexander Rakhlin Ayush Sekhari OffRL 57 9 0 25 Nov 2022
Multi-armed Bandit Learning on a Graph Tianpeng Zhang Kasper Johansson Na Li 44 6 0 20 Sep 2022
Making Linear MDPs Practical via Contrastive Representation Learning Tianjun Zhang Tongzheng Ren Mengjiao Yang Joseph E. Gonzalez Dale Schuurmans Bo Dai 30 44 0 14 Jul 2022
Provably Efficient Kernelized Q-Learning Shuang Liu H. Su MLT 49 4 0 21 Apr 2022
Branching Reinforcement Learning Yihan Du Wei Chen 32 0 0 16 Feb 2022
Continual Learning In Environments With Polynomial Mixing Times Matthew D Riemer Sharath Chandra Raparthy Ignacio Cases G. Subbaraj M. P. Touzel Irina Rish CLL 46 8 0 13 Dec 2021
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning Tongzheng Ren Tianjun Zhang Csaba Szepesvári Bo Dai 39 20 0 22 Nov 2021
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning Yuanzhi Li Ruosong Wang Lin F. Yang 44 20 0 01 Nov 2021
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs Han Zhong Zhuoran Yang Zhaoran Wang Csaba Szepesvári 66 21 0 18 Oct 2021
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning Gen Li Laixi Shi Yuxin Chen Yuejie Chi OffRL 49 51 0 09 Oct 2021
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments Amin Rakhsha Xuezhou Zhang Xiaojin Zhu Adish Singla AAML OffRL 44 37 0 16 Feb 2021
Robust Policy Gradient against Strong Data Corruption Xuezhou Zhang Yiding Chen Xiaojin Zhu Wen Sun AAML 57 38 0 11 Feb 2021
Learning Adversarial Markov Decision Processes with Delayed Feedback Tal Lancewicki Aviv A. Rosenberg Yishay Mansour 43 32 0 29 Dec 2020
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play Qinghua Liu Tiancheng Yu Yu Bai Chi Jin 41 121 0 04 Oct 2020
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs Jiafan He Dongruo Zhou Quanquan Gu 40 37 0 01 Oct 2020
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon Zihan Zhang Xiangyang Ji S. Du OffRL 57 104 0 28 Sep 2020
A Unifying View of Optimism in Episodic Reinforcement Learning Gergely Neu Ciara Pike-Burke 22 66 0 03 Jul 2020
Adaptive Discretization for Model-Based Reinforcement Learning Sean R. Sinclair Tianyu Wang Gauri Jain Siddhartha Banerjee Chao Yu OffRL 24 21 0 01 Jul 2020
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping Dongruo Zhou Jiafan He Quanquan Gu 35 134 0 23 Jun 2020
Near-Optimal Reinforcement Learning with Self-Play Yunru Bai Chi Jin Tiancheng Yu 29 130 0 22 Jun 2020
$Q$ -learning with Logarithmic Regret Kunhe Yang Lin F. Yang S. Du 48 59 0 16 Jun 2020
Model-Based Reinforcement Learning with Value-Targeted Regression Alex Ayoub Zeyu Jia Csaba Szepesvári Mengdi Wang Lin F. Yang OffRL 62 300 0 01 Jun 2020
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension Ruosong Wang Ruslan Salakhutdinov Lin F. Yang 30 55 0 21 May 2020
Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium Qiaomin Xie Yudong Chen Zhaoran Wang Zhuoran Yang 46 125 0 17 Feb 2020
Provable Self-Play Algorithms for Competitive Reinforcement Learning Yu Bai Chi Jin SSL 32 149 0 10 Feb 2020
Making Sense of Reinforcement Learning and Probabilistic Inference Brendan O'Donoghue Ian Osband Catalin Ionescu OffRL 37 48 0 03 Jan 2020
Influence-Based Multi-Agent Exploration Tonghan Wang Jianhao Wang Yi Wu Chongjie Zhang 26 138 0 12 Oct 2019
Provably Efficient Reinforcement Learning with Linear Function Approximation Chi Jin Zhuoran Yang Zhaoran Wang Michael I. Jordan 57 546 0 11 Jul 2019
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies Yonathan Efroni Nadav Merlis Mohammad Ghavamzadeh Shie Mannor OffRL 29 68 0 27 May 2019
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound Lin F. Yang Mengdi Wang OffRL GP 26 283 0 24 May 2019
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds Andrea Zanette Emma Brunskill OffRL 56 273 0 01 Jan 2019
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning David Janz Jiri Hron Przemysław Mazur Katja Hofmann José Miguel Hernández-Lobato Sebastian Tschiatschek 65 51 0 15 Oct 2018
Exploration in Structured Reinforcement Learning Jungseul Ok Alexandre Proutiere Damianos Tranos 40 62 0 03 Jun 2018
Variance Reduction Methods for Sublinear Reinforcement Learning Sham Kakade Mengdi Wang Lin F. Yang 21 32 0 26 Feb 2018
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning Ronan Fruit Matteo Pirotta A. Lazaric R. Ortner 29 115 0 12 Feb 2018
The Uncertainty Bellman Equation and Exploration Brendan O'Donoghue Ian Osband Rémi Munos Volodymyr Mnih 41 189 0 15 Sep 2017
Deep Exploration via Randomized Value Functions Ian Osband Benjamin Van Roy Daniel Russo Zheng Wen 53 300 0 22 Mar 2017
Minimax Regret Bounds for Reinforcement Learning M. G. Azar Ian Osband Rémi Munos 36 766 0 16 Mar 2017
Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning Yichen Chen Mengdi Wang 37 64 0 08 Dec 2016
Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband Benjamin Van Roy BDL 29 255 0 01 Jul 2016