Results 11 to 20 of about 5,876,040 (331)
Reinforcement Learning and Physics [PDF]
Applied Sciences, 2021 Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the ...
José D. Martín-Guerrero, Lucas Lamata
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, 2020 Chapter in "A Guided Tour of Artificial Intelligence Research ...
Buffet, Olivier +2 more
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RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 Prompting has shown impressive success in enabling large pre-trained language models (LMs) to perform diverse NLP tasks, especially with only few downstream data. Automatically finding the optimal prompt for each task, however, is challenging.
Mingkai Deng +8 more
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Frontiers in Artificial Intelligence, 2022 Within computational reinforcement learning, a growing body of work seeks to express an agent's knowledge of its world through large collections of predictions.
Alex Kearney +5 more
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Survey of Reinforcement Learning Based Recommender Systems [PDF]
Jisuanji kexue, 2021 Recommender systems are devoted to find and automatically recommend valuable information and services for users from massive data,which can effectively solve the information overload problem,and become an important information technology in the era of ...
YU Li, DU Qi-han, YUE Bo-yan, XIANG Jun-yao, XU Guan-yu, LENG You-fang
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IEEE Access, 2023 In this paper, a quality diversity optimization method (QDOM) based on an adaptive bound-searching algorithm and diversity-selecting immune algorithm is proposed for solving bilinear matrix inequality (BMI) problems in control system design. By using the
Shiuan-Yeh Chen +2 more
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Deep Reinforcement Learning with Double Q-Learning [PDF]
AAAI Conference on Artificial Intelligence, 2015 The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be ...
H. V. Hasselt, A. Guez, David Silver
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A Definition of Continual Reinforcement Learning [PDF]
Neural Information Processing Systems, 2023 In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward. However, this perspective is based on a restricted view of learning as finding a solution, rather than treating
David Abel +5 more
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The Journal of Neuroscience, 2021 Humans routinely learn the value of actions by updating their expectations based on past outcomes – a process driven by reward prediction errors (RPEs). Importantly, however, implementing a course of action also requires the investment of effort. Recent work has revealed a close link between the neural signals involved in effort exertion and those ...
Huw Jarvis +5 more
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Reinforcement Symbolic Learning [PDF]
, 2021 Complex problem solving involves representing structured knowledge, reasoning and learning, all at once. In this prospective study, we make explicit how a reinforcement learning paradigm can be applied to a symbolic representation of a concrete problem-solving task, modeled here by an ontology.
Mercier, Chloé +2 more
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