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Reinforcement Learning, Bit by Bit

2021-03-06 06:37:46
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen
arXiv_AI
       
Abstract

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 develop concepts and establish a regret bound that together offer principled guidance. The bound sheds light on questions of what information to seek, how to seek that information, and it what information to retain. To illustrate concepts, we design simple agents that build on them and present computational results that demonstrate improvements in data efficiency.

Abstract (translated)

URL

https://arxiv.org/abs/2103.04047

PDF

https://arxiv.org/pdf/2103.04047.pdf

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