Results 41 to 50 of about 5,876,040 (331)
Inverse Reinforcement Learning without Reinforcement Learning
, 2023 Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunately, traditional IRL methods suffer from a computational weakness: they require repeatedly solving a hard reinforcement learning (RL) problem as a subroutine. This is counter-
Swamy, Gokul +3 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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Hierarchical Reinforcement Learning: A Survey and Open Research Challenges
Machine Learning and Knowledge Extraction, 2022 Reinforcement learning (RL) allows an agent to solve sequential decision-making problems by interacting with an environment in a trial-and-error fashion. When these environments are very complex, pure random exploration of possible solutions often fails,
Matthias Hutsebaut-Buysse +2 more
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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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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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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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Learning to reinforcement learn
, 2016 17 pages, 7 figures, 1 ...
Wang, Jane +8 more
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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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