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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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Survey on Knowledge Transfer Method in Deep Reinforcement Learning [PDF]
Jisuanji kexue, 2023 Deep reinforcement learning is a hot issue in artificial intelligence research.With the deepening of research,some shortcomings are gradually exposed,such as low data utilization,weak generalization ability,difficult exploration,lack of reasoning and ...
ZHANG Qiyang, CHEN Xiliang, CAO Lei, LAI Jun, SHENG Lei
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Learning an Accurate State Transition Dynamics Model by Fitting Both a Function and its Derivative
IEEE Access, 2022 Learning accurate state transition dynamics model in a sample-efficient way is important to predict the future states from the current states and actions of a system both accurately and efficiently in model-based reinforcement learning for many robotic ...
Youngho Kim, Hoosang Lee, Jeha Ryu
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IEEE Access, 2021 A well-designed demand response (DR) program is essential in smart home to optimize energy usage according to user preferences. In this study, we proposed a multiobjective reinforcement learning (MORL) algorithm to design a DR program.
Song-Jen Chen, Wei-Yu Chiu, Wei-Jen Liu
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IEEE Access, 2022 Snake-like modular robots (MRs) are highly flexible, but, to traverse a challenging terrain or explore a region of interest, MR needs to attain efficient locomotion depending on a tradeoff between objectives like forward velocity and power consumption of
Akash Singh +3 more
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Frontiers in Psychiatry, 2023 IntroductionMany American employers seek to alleviate employee mental health symptoms through resources like employee assistance programs (EAPs), yet these programs are often underutilized.
Ashley B. West +4 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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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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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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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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