Results 31 to 40 of about 198,528 (317)
sadyan9123/Inverse-Reinforcement-Learning V1.0
, 2021 Implementations of selected inverse reinforcement learning ...
Matthew Alger
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On the Use of Deep Reinforcement Learning for Visual Tracking: A Survey
IEEE Access, 2021 This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning.
Giorgio Cruciata +2 more
doaj +1 more source
Frontiers in Psychology, 2021 Reinforcement learning relies on the reward prediction error (RPE) signals conveyed by the midbrain dopamine system. Previous studies showed that dopamine plays an important role in both positive and negative reinforcement.
Shuyuan Xu +6 more
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Reinforcement Learning to Rank [PDF]
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019 Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users. Instead, series of exchanges between the user and the system are becoming mainstream, especially when users have complex needs or when the system struggles to understand the user's intent.
openaire +2 more sources
Analysis and method comparsion of online and offline reinforcement learning [PDF]
ITM Web of ConferencesIn this paper, an exploration of the online and offline precepts of reinforcement learning and the associated algorithms of paradigms is carried out in a systematic manner.
Zheng Changhang
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Reinforcement Learning: A Survey
Journal of Artificial Intelligence Research, 1996 This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized.
Leslie Pack Kaelbling +2 more
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Safe and Efficient Operation with Constrained Hierarchical Reinforcement Learning
, 2023 Hierarchical Reinforcement Learning (HRL) holds the promise of enhancing sample efficiency and generalization capabilities of Reinforcement Learning (RL) agents by leveraging task decomposition and temporal abstraction, which aligns with human reasoning.
Günnemann, Stephan +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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Sample Efficient Reinforcement Learning with REINFORCE
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory. However, prior works have either required exact gradients or state-action visitation measure based mini-batch stochastic gradients ...
Junzi Zhang +3 more
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