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Corruption-Robust Offline Reinforcement Learning with General Function Approximation
Chenlu Ye
, Rui Yang
, Quanquan Gu
,
Tong Zhang
Research output
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Contribution to journal
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Conference article
›
peer-review
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Keyphrases
Corruption
100%
Function Approximation
100%
General Functioning
100%
Offline Reinforcement Learning
100%
Markov Decision Process
42%
Uncertainty Weighting
28%
Tight
14%
Regularization Parameter
14%
Weighting Method
14%
Robust Algorithm
14%
Feature Map
14%
Data Distribution
14%
Learning Settings
14%
Iterative Methods
14%
Corrupt
14%
Error Term
14%
Optimal Policy
14%
Adversary
14%
Additive Factors
14%
Confidence Set
14%
Dependent Errors
14%
Corruption Level
14%
Interactive Reinforcement Learning
14%
Corruption Robustness
14%
Mathematics
Approximation Function
100%
Markov Decision Process
100%
Minimizes
33%
Regularization
33%
Error Term
33%
Optimal Policy
33%
Robust Algorithm
33%
Data Distribution
33%
Confidence Set
33%
Computer Science
Reinforcement Learning
100%
Function Approximation
100%
Markov Decision Process
75%
Regularization Parameter
25%
Data Distribution
25%
Underlying Data
25%
Feature Map
25%