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Reinforcement Learning, Bit by Bit

delete2023-01-01
delete6
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OA
AI
X
Xiuyuan Lu *
B
Benjamin Van Roy
V
Vikranth Dwaracherla
M
Morteza Ibrahimi
I
Ian Osband
Z
Zheng Wen
DOI：10.1561/2200000097delete
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Abstract

Abstract

En 中文
Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency poses an impediment to carrying this success over to real environments. The design of data-efficient agents calls for a deeper understanding of information acquisition and representation. We discuss concepts and regret analysis that together offer principled guidance. This line of thinking sheds light on questions of what information to seek, how to seek that information, and what information to retain. To illustrate concepts, we design simple agents that build on them and present computational results that highlight data efficiency.
Keywords:
ALLOCATION
ALGORITHM
INFORMATION
BOUNDS

Journal

Foundations and Trends in Machine Learning cover
Foundations and Trends in Machine Learning
IF:
25.4
Papers:
35
Citations:
4.7K

Organization

No organization information available