Model-based Bayesian Reinforcement Learning in Factored Markov Decision Process

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Provided by: Academy Publisher
Topic: Big Data
Format: PDF
Learning the enormous number of parameters is a challenging problem in model-based Bayesian reinforcement learning. In order to solve the problem, the authors propose a model-based Factored Bayesian Reinforcement Learning (F-BRL) approach. F-BRL exploits a factored representation to describe states to reduce the number of parameters. Representing the conditional independence relationships between state features using dynamic Bayesian networks, F-BRL adopts Bayesian inference method to learn the unknown structure and parameters of the Bayesian networks simultaneously.
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