Correlation minimizing replay memory in temporal-difference reinforcement learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00339076" target="_blank" >RIV/68407700:21230/20:00339076 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.neucom.2020.02.004" target="_blank" >https://doi.org/10.1016/j.neucom.2020.02.004</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2020.02.004" target="_blank" >10.1016/j.neucom.2020.02.004</a>

Result language
angličtina
Original language name
Correlation minimizing replay memory in temporal-difference reinforcement learning
Original language description
Online reinforcement learning agents are now able to process an increasing amount of data which makes their approximation and compression into value functions a more demanding task. To improve approximation, thus the learning process itself, it has been proposed to select randomly a mini-batch of the past experiences that are stored in the replay memory buffer to be replayed at each learning step. In this work, we present an algorithm that classifies and samples the experiences into separate contextual memory buffers using an unsupervised learning technique. This allows each new experience to be associated to a mini-batch of the past experiences that are not from the same contextual buffer as the current one, thus further reducing the correlation between experiences. Experimental results show that the correlation minimizing sampling improves over Q-learning algorithms with uniform sampling, and that a significant improvement can be observed when coupled with the sampling methods that prioritize on the experience temporal difference error.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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