Results 21 to 30 of about 544,487 (316)
Photonic reinforcement learning based on optoelectronic reservoir computing
Scientific Reports, 2022 Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot control, internet advertising, and elastic optical networks.
Kazutaka Kanno, Atsushi Uchida
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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
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On the convergence of reinforcement learning [PDF]
Journal of Economic Theory, 2005 Abstract This paper examines the convergence of payoffs and strategies in Erev and Roth's model of reinforcement learning. When all players use this rule it eliminates iteratively dominated strategies and in two-person constant-sum games average payoffs converge to the value of the game.
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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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Relational reinforcement learning [PDF]
, 1998 Relational reinforcement learning is presented, a learning technique that combines reinforcement learning with relational learning or inductive logic programming. Due to the use of a more expressive representation language to represent states, actions and q-functions, relational reinforcement learning can be potentially applied to a new range of ...
Džeroski, Sašo +2 more
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Reactive Reinforcement Learning in Asynchronous Environments
Frontiers in Robotics and AI, 2018 The relationship between a reinforcement learning (RL) agent and an asynchronous environment is often ignored. Frequently used models of the interaction between an agent and its environment, such as Markov Decision Processes (MDP) or Semi-Markov Decision
Jaden B. Travnik +6 more
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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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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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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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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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