Results 31 to 40 of about 683,294 (301)
Human Dorsal Striatal Activity during Choice Discriminates Reinforcement Learning Behavior from the Gambler’s Fallacy [PDF]
, 2011 Reinforcement learning theory has generated substantial interest in neurobiology, particularly because of the resemblance between phasic dopamine and reward prediction errors.
Jessup, Ryan K., O'Doherty, John P.
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Review of Attention Mechanisms in Reinforcement Learning [PDF]
Jisuanji kexue yu tansuoIn recent years, the combination of reinforcement learning and attention mechanisms has attracted an increasing attention in algorithmic research field.
XIA Qingfeng, XU Ke'er, LI Mingyang, HU Kai, SONG Lipeng, SONG Zhiqiang, SUN Ning
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Journal of the American Statistical Association, 2015 In this article, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman 2001) under high-dimensional settings. The innovations are three-fold.
Ruoqing Zhu +2 more
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Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
IEEE Access, 2023 While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to ...
Maziar Gomrokchi +4 more
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The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI [PDF]
, 2020 After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures.
Alexander, Samuel Allen
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Quantum Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008 13 pages, 7 figures ...
Han-Xiong Li +3 more
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Bayesian Reinforcement Learning [PDF]
, 2012 This chapter surveys recent lines of work that use Bayesian techniques for reinforcement learning. In Bayesian learning, uncertainty is expressed by a prior distribution over unknown parameters and learning is achieved by computing a posterior distribution based on the data observed.
Vlassis, Nikos +3 more
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Reinforcement Learning Approaches in Social Robotics
Sensors, 2021 This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior.
Neziha Akalin, Amy Loutfi
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Learning to reinforcement learn
, 2016 17 pages, 7 figures, 1 ...
Wang, Jane +8 more
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A survey of benchmarks for reinforcement learning algorithms
South African Computer Journal, 2020 Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly.
Belinda Stapelberg, Katherine Mary Malan
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