ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2011.04622
  4. Cited By
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

9 November 2020
Zhuoran Yang
Chi Jin
Zhaoran Wang
Mengdi Wang
Michael I. Jordan
ArXivPDFHTML

Papers citing "On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces"

16 / 16 papers shown
Title
Sequential Information Design: Markov Persuasion Process and Its
  Efficient Reinforcement Learning
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
Jibang Wu
Zixuan Zhang
Zhe Feng
Zhaoran Wang
Zhuoran Yang
Michael I. Jordan
Haifeng Xu
18
33
0
22 Feb 2022
Off-Policy Fitted Q-Evaluation with Differentiable Function
  Approximators: Z-Estimation and Inference Theory
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
Ruiqi Zhang
Xuezhou Zhang
Chengzhuo Ni
Mengdi Wang
OffRL
16
16
0
10 Feb 2022
Provably Efficient Generative Adversarial Imitation Learning for Online
  and Offline Setting with Linear Function Approximation
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
Zhihan Liu
Yufeng Zhang
Zuyue Fu
Zhuoran Yang
Zhaoran Wang
OffRL
25
6
0
19 Aug 2021
Neural Contextual Bandits without Regret
Neural Contextual Bandits without Regret
Parnian Kassraie
Andreas Krause
OffRL
22
38
0
07 Jul 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
32
42
0
18 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
OffRL
OnRL
35
161
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
26
50
0
07 Jun 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
24
28
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
32
52
0
24 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
30
187
0
19 Mar 2021
Model-free Representation Learning and Exploration in Low-rank MDPs
Model-free Representation Learning and Exploration in Low-rank MDPs
Aditya Modi
Jinglin Chen
A. Krishnamurthy
Nan Jiang
Alekh Agarwal
OffRL
102
78
0
14 Feb 2021
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve
  Optimism, Embrace Virtual Curvature
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature
Kefan Dong
Jiaqi Yang
Tengyu Ma
24
32
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
29
212
0
01 Feb 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
122
166
0
06 Jan 2021
Is Pessimism Provably Efficient for Offline RL?
Is Pessimism Provably Efficient for Offline RL?
Ying Jin
Zhuoran Yang
Zhaoran Wang
OffRL
27
346
0
30 Dec 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
132
135
0
09 Dec 2019
1