Reinforcement learning algorithms: A brief survey

•RL can be used to solve problems involving sequential decision-making.•RL is based on trial-and-error learning through rewards and punishments.•The ultimate goal of an RL agent is to maximize cumulative reward.•RL agent tries to learn the optimal value and policy functions.•DNN-based function appro...

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Veröffentlicht in:	Expert systems with applications Jg. 231; S. 120495
Hauptverfasser:	Shakya, Ashish Kumar, Pillai, Gopinatha, Chakrabarty, Sohom
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 30.11.2023
Schlagworte:	Deep Reinforcement Learning (DRL) Function approximation Reinforcement learning Stochastic optimal control Deep Reinforcement Learning (DRL) Function approximation Stochastic optimal control Reinforcement learning
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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