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What can online reinforcement learning with function approximation
  benefit from general coverage conditions?

What can online reinforcement learning with function approximation benefit from general coverage conditions?

25 April 2023
Fanghui Liu
Luca Viano
V. Cevher
    OffRL
ArXivPDFHTML

Papers citing "What can online reinforcement learning with function approximation benefit from general coverage conditions?"

4 / 4 papers shown
Title
A General Framework for Sample-Efficient Function Approximation in
  Reinforcement Learning
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Zixiang Chen
C. J. Li
An Yuan
Quanquan Gu
Michael I. Jordan
OffRL
108
26
0
30 Sep 2022
Understanding Deep Neural Function Approximation in Reinforcement
  Learning via $ε$-Greedy Exploration
Understanding Deep Neural Function Approximation in Reinforcement Learning via εεε-Greedy Exploration
Fanghui Liu
Luca Viano
V. Cevher
29
16
0
15 Sep 2022
Pessimistic Model-based Offline Reinforcement Learning under Partial
  Coverage
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
Masatoshi Uehara
Wen Sun
OffRL
96
144
0
13 Jul 2021
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on
  Open Problems
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
1