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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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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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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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Teacher Skills to Provide GMIM Ranoketang Elementary Students’ Reinforcement
Primary: Jurnal Pendidikan Guru Sekolah Dasar, 2023 In the learning process at school, a teacher must have the skills to provide reinforcement in teaching in order to achieve the expected learning goals.
Romi Mesra +6 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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