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Learning Near Optimal Policies with Low Inherent Bellman Error
v1v2v3 (latest)

Learning Near Optimal Policies with Low Inherent Bellman Error

29 February 2020
Andrea Zanette
A. Lazaric
Mykel Kochenderfer
Emma Brunskill
    OffRL
ArXiv (abs)PDFHTML

Papers citing "Learning Near Optimal Policies with Low Inherent Bellman Error"

44 / 94 papers shown
Title
Optimistic Policy Optimization is Provably Efficient in Non-stationary
  MDPs
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs
Han Zhong
Zhuoran Yang
Zhaoran Wang
Csaba Szepesvári
117
21
0
18 Oct 2021
Reward-Free Model-Based Reinforcement Learning with Linear Function
  Approximation
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation
Weitong Zhang
Dongruo Zhou
Quanquan Gu
OffRL
86
28
0
12 Oct 2021
Representation Learning for Online and Offline RL in Low-rank MDPs
Representation Learning for Online and Offline RL in Low-rank MDPs
Masatoshi Uehara
Xuezhou Zhang
Wen Sun
OffRL
140
129
0
09 Oct 2021
Provable Benefits of Actor-Critic Methods for Offline Reinforcement
  Learning
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning
Andrea Zanette
Martin J. Wainwright
Emma Brunskill
OffRL
100
119
0
19 Aug 2021
Efficient Local Planning with Linear Function Approximation
Efficient Local Planning with Linear Function Approximation
Dong Yin
Botao Hao
Yasin Abbasi-Yadkori
N. Lazić
Csaba Szepesvári
132
19
0
12 Aug 2021
Towards General Function Approximation in Zero-Sum Markov Games
Towards General Function Approximation in Zero-Sum Markov Games
Baihe Huang
Jason D. Lee
Zhaoran Wang
Zhuoran Yang
88
47
0
30 Jul 2021
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Baihe Huang
Kaixuan Huang
Sham Kakade
Jason D. Lee
Qi Lei
Runzhe Wang
Jiaqi Yang
101
8
0
14 Jul 2021
Adapting to Misspecification in Contextual Bandits
Adapting to Misspecification in Contextual Bandits
Dylan J. Foster
Claudio Gentile
M. Mohri
Julian Zimmert
117
87
0
12 Jul 2021
Variance-Aware Off-Policy Evaluation with Linear Function Approximation
Variance-Aware Off-Policy Evaluation with Linear Function Approximation
Yifei Min
Tianhao Wang
Dongruo Zhou
Quanquan Gu
OffRL
89
38
0
22 Jun 2021
Provably Efficient Representation Selection in Low-rank Markov Decision
  Processes: From Online to Offline RL
Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL
Weitong Zhang
Jiafan He
Dongruo Zhou
Amy Zhang
Quanquan Gu
OffRL
72
11
0
22 Jun 2021
Uniform-PAC Bounds for Reinforcement Learning with Linear Function
  Approximation
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation
Jiafan He
Dongruo Zhou
Quanquan Gu
45
13
0
22 Jun 2021
MADE: Exploration via Maximizing Deviation from Explored Regions
MADE: Exploration via Maximizing Deviation from Explored Regions
Tianjun Zhang
Paria Rashidinejad
Jiantao Jiao
Yuandong Tian
Joseph E. Gonzalez
Stuart J. Russell
OffRL
96
44
0
18 Jun 2021
Randomized Exploration for Reinforcement Learning with General Value
  Function Approximation
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Haque Ishfaq
Qiwen Cui
V. Nguyen
Alex Ayoub
Zhuoran Yang
Zhaoran Wang
Doina Precup
Lin F. Yang
94
48
0
15 Jun 2021
Policy Finetuning: Bridging Sample-Efficient Offline and Online
  Reinforcement Learning
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
Tengyang Xie
Nan Jiang
Huan Wang
Caiming Xiong
Yu Bai
OffRLOnRL
109
165
0
09 Jun 2021
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
Chi Jin
Qinghua Liu
Tiancheng Yu
86
50
0
07 Jun 2021
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs
  with a Generative Model
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model
Bingyan Wang
Yuling Yan
Jianqing Fan
108
20
0
28 May 2021
Sample-Efficient Reinforcement Learning Is Feasible for Linearly
  Realizable MDPs with Limited Revisiting
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Gen Li
Yuxin Chen
Yuejie Chi
Yuantao Gu
Yuting Wei
OffRL
90
30
0
17 May 2021
Cautiously Optimistic Policy Optimization and Exploration with Linear
  Function Approximation
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation
Andrea Zanette
Ching-An Cheng
Alekh Agarwal
106
53
0
24 Mar 2021
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant
  Suboptimality Gap
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap
Yuanhao Wang
Ruosong Wang
Sham Kakade
OffRL
191
43
0
23 Mar 2021
Bilinear Classes: A Structural Framework for Provable Generalization in
  RL
Bilinear Classes: A Structural Framework for Provable Generalization in RL
S. Du
Sham Kakade
Jason D. Lee
Shachar Lovett
G. Mahajan
Wen Sun
Ruosong Wang
OffRL
216
191
0
19 Mar 2021
Near-optimal Policy Optimization Algorithms for Learning Adversarial
  Linear Mixture MDPs
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs
Jiafan He
Dongruo Zhou
Quanquan Gu
139
24
0
17 Feb 2021
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov
  Games
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games
Zixiang Chen
Dongruo Zhou
Quanquan Gu
75
25
0
15 Feb 2021
Simple Agent, Complex Environment: Efficient Reinforcement Learning with
  Agent States
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States
Shi Dong
Benjamin Van Roy
Zhengyuan Zhou
107
32
0
10 Feb 2021
Near-optimal Representation Learning for Linear Bandits and Linear RL
Near-optimal Representation Learning for Linear Bandits and Linear RL
Jiachen Hu
Xiaoyu Chen
Chi Jin
Lihong Li
Liwei Wang
OffRL
165
53
0
08 Feb 2021
Bellman Eluder Dimension: New Rich Classes of RL Problems, and
  Sample-Efficient Algorithms
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms
Chi Jin
Qinghua Liu
Sobhan Miryoosefi
OffRL
123
220
0
01 Feb 2021
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear
  Mixture MDP
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Zihan Zhang
Jiaqi Yang
Xiangyang Ji
S. Du
114
41
0
29 Jan 2021
Provably Efficient Reinforcement Learning with Linear Function
  Approximation Under Adaptivity Constraints
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
Chi Jin
Zhuoran Yang
Zhaoran Wang
OffRL
266
169
0
06 Jan 2021
A Provably Efficient Algorithm for Linear Markov Decision Process with
  Low Switching Cost
A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost
Minbo Gao
Tianle Xie
S. Du
Lin F. Yang
81
46
0
02 Jan 2021
Learning Adversarial Markov Decision Processes with Delayed Feedback
Learning Adversarial Markov Decision Processes with Delayed Feedback
Tal Lancewicki
Aviv A. Rosenberg
Yishay Mansour
109
35
0
29 Dec 2020
Regret Bound Balancing and Elimination for Model Selection in Bandits
  and RL
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL
Aldo Pacchiano
Christoph Dann
Claudio Gentile
Peter L. Bartlett
100
49
0
24 Dec 2020
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov
  Decision Processes
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
Dongruo Zhou
Quanquan Gu
Csaba Szepesvári
110
209
0
15 Dec 2020
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can
  be Exponentially Harder than Online RL
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
Andrea Zanette
OffRL
219
71
0
14 Dec 2020
Logarithmic Regret for Reinforcement Learning with Linear Function
  Approximation
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Jiafan He
Dongruo Zhou
Quanquan Gu
60
95
0
23 Nov 2020
Model-based Reinforcement Learning for Continuous Control with Posterior
  Sampling
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
Ying Fan
Yifei Ming
97
18
0
20 Nov 2020
On Function Approximation in Reinforcement Learning: Optimism in the
  Face of Large State Spaces
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
Zhuoran Yang
Chi Jin
Zhaoran Wang
Mengdi Wang
Michael I. Jordan
97
18
0
09 Nov 2020
Efficient Learning in Non-Stationary Linear Markov Decision Processes
Efficient Learning in Non-Stationary Linear Markov Decision Processes
Ahmed Touati
Pascal Vincent
108
29
0
24 Oct 2020
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal
  Algorithm Escaping the Curse of Horizon
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
Zihan Zhang
Xiangyang Ji
S. Du
OffRL
128
107
0
28 Sep 2020
Provably Efficient Reward-Agnostic Navigation with Linear Value
  Iteration
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
Andrea Zanette
A. Lazaric
Mykel J. Kochenderfer
Emma Brunskill
98
64
0
18 Aug 2020
Learning Infinite-horizon Average-reward MDPs with Linear Function
  Approximation
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
Chen-Yu Wei
Mehdi Jafarnia-Jahromi
Haipeng Luo
Rahul Jain
94
43
0
23 Jul 2020
Provably Efficient Reinforcement Learning for Discounted MDPs with
  Feature Mapping
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
Dongruo Zhou
Jiafan He
Quanquan Gu
105
136
0
23 Jun 2020
Reinforcement Learning with General Value Function Approximation:
  Provably Efficient Approach via Bounded Eluder Dimension
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension
Ruosong Wang
Ruslan Salakhutdinov
Lin F. Yang
101
55
0
21 May 2020
Model Selection in Contextual Stochastic Bandit Problems
Model Selection in Contextual Stochastic Bandit Problems
Aldo Pacchiano
My Phan
Yasin Abbasi-Yadkori
Anup B. Rao
Julian Zimmert
Tor Lattimore
Csaba Szepesvári
203
94
0
03 Mar 2020
Optimism in Reinforcement Learning with Generalized Linear Function
  Approximation
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Yining Wang
Ruosong Wang
S. Du
A. Krishnamurthy
191
137
0
09 Dec 2019
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
Andrea Zanette
David Brandfonbrener
Emma Brunskill
Matteo Pirotta
A. Lazaric
153
132
0
01 Nov 2019
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