Results 21 to 30 of about 6,531,013 (293)

Feasibility Analysis and Application of Reinforcement Learning Algorithm Based on Dynamic Parameter Adjustment

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, Xueqiang Gu, Chengyi Zeng, Yuan Feng   +3 more
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Relational reinforcement learning [PDF]

Machine Learning, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Saso Dzeroski, Luc De Raedt, Kurt Driessens   +2 more
openaire   +6 more sources

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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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, Yukai Feng, Jian Wang, Zhengxing Wu, Min Tan, Junzhi Yu   +5 more
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Reinforcement Learning

, 2020
Chapter in "A Guided Tour of Artificial Intelligence Research ...
Buffet, Olivier, Pietquin, Olivier, Weng, Paul   +2 more
openaire   +3 more sources

Performance characterization of reinforcement learning-enabled evolutionary algorithms for integrated school bus routing and scheduling problem

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, Abhishek R. Hegde, Haresh P. Pandiarajan, Bharadwaj Veeravalli   +3 more
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A neural network model for the orbitofrontal cortex and task space acquisition during reinforcement learning. [PDF]

PLoS Computational Biology, 2018
Reinforcement learning has been widely used in explaining animal behavior. In reinforcement learning, the agent learns the value of the states in the task, collectively constituting the task state space, and uses the knowledge to choose actions and ...
Zhewei Zhang, Zhenbo Cheng, Zhongqiao Lin, Chechang Nie, Tianming Yang   +4 more
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Quantum Reinforcement Learning

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008
13 pages, 7 figures ...
Daoyi Dong, Chunlin Chen 0001, Han-Xiong Li, Tzyh Jong Tarn   +3 more
openaire   +4 more sources

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, Jaden B. Travnik, Kory W. Mathewson, Kory W. Mathewson, Richard S. Sutton, Patrick M. Pilarski, Patrick M. Pilarski   +6 more
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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
doaj   +1 more source