Results 11 to 20 of about 197,676 (281)
Reinforcement Learning and Physics
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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Relational reinforcement learning [PDF]
Machine Learning, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dzeroski, Saso +2 more
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Sensors, 2023 Reinforcement learning agents that have not been seen during training must be robust in test environments. However, the generalization problem is challenging to solve in reinforcement learning using high-dimensional images as the input. The addition of a
Sanghoon Park +4 more
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Reinforcement Learning during Locomotion
eneuro, 2023 When learning a new motor skill, people often must use trial and error to discover which movement is best. In the reinforcement learning framework, this concept is known as exploration and has been linked to increased movement variability in motor tasks. For locomotor tasks, however, increased variability decreases upright stability.
Jonathan M Wood +2 more
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A Survey on Reinforcement Learning Methods in Bionic Underwater Robots
Biomimetics, 2023 Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope.
Ru Tong +5 more
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Risk-Sensitive Reinforcement Learning [PDF]
Neural Computation, 2014 We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of ...
Shen, Yun +3 more
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Algorithms, 2020 Reinforcement learning, as a branch of machine learning, has been gradually applied in the control field. However, in the practical application of the algorithm, the hyperparametric approach to network settings for deep reinforcement learning still ...
Menglin Li +3 more
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Reinforcement Learning for Bioretrosynthesis [PDF]
ACS Synthetic Biology, 2019 AbstractMetabolic engineering aims to produce chemicals of interest from living organisms, to advance towards greener chemistry. Despite efforts, the research and development process is still long and costly and efficient computational design tools are required to explore the chemical biosynthetic space.
Koch, Mathilde +2 more
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Review of Model-Based Reinforcement Learning
Jisuanji kexue yu tansuo, 2020 Deep reinforcement learning (DRL) as an important learning paradigm in the field of machine learning, has received increasing attentions after AlphaGo defeats the human.
ZHAO Tingting, KONG Le, HAN Yajie, REN Dehua, CHEN Yarui
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International Journal of Cognitive Computing in Engineering, 2021 Bi-objective school bus scheduling optimization problem that is a subset of vehicle fleet scheduling problem is focused in this paper. In the literature, school bus routing and scheduling problem is proven to be an NP-Hard problem.
Eda Koksal +3 more
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