Reinforcement Learning based Optimization of Bayesian Networks for Generating Feasible Vehicle Configuration Suggestions

Dürr, Simon; Lamprecht, Raphael; Kauffmann, Matthias; Huber, Marco F.

doi:10.1109/case49439.2021.9551428

Cited by 2 publications

(1 citation statement)

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“…In addition to the directions mentioned above, the combination of Bayesian methods and reinforcement learning has a broader range of applications, including recommendation systems Dürr et al (2021), cyber security Allen et al (2018), automatic driving and wireless on-board systems Gharaee et al (2021); Liang et al (2021), biomedical science Imani and Braga-Neto (2018); Choi and Cho (2020); Rathore and Samant (2021), blockchain Asheralieva and Niyato (2020), the industrial spectrum Liu et al (2020), control systems Ouyang et al (2019). Application needs and the main objective of algorithm research complement each other.…”

Section: Applicationsmentioning

confidence: 99%

Towards Uncertainty in Decision: A Survey on Recent Advances and Challenges in Bayesian Reinforcement Learning

Wang

Meng

Zhou

et al. 2022

Preprint

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Reinforcement learning is a research paradigm that is commonly utilized to tackle problems involving sequential decision-making. Agents learn optimum policy from samples generated by interacting with uncertain environments. Bayesian reinforcement learning presents the uncertainty measure of probability significance based on sequential decision-making, which plays a proper auxiliary function in the optimization of agent control policies, attracting a lot of study attention. This paper provides an overview of contemporary Bayesian and reinforcement learning methods in this setting. We begin by outlining the many types of reinforcement learning and Bayesian approaches, as well as their characteristics. Then we review the most sophisticated Bayesian reinforcement learning approaches from the perspective of uncertainty measurement and control, as well as summarize and discuss various challenges and related research in its expansion to complicated control tasks. Applying the Bayesian technique with reinforcement learning in diverse disciplines are then discussed. Finally, we highlight the current challenges of Bayesian reinforcement learning and future research topics that might help promote Bayesian reinforcement learning.

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