Due to the enormous number of parameters and slow convergence which are the major obstacles for online learning in model-based Bayesian reinforcement learning

the paper presents a model-based factored Bayesian reinforcement learning approach.Firstly

factored representations are made to represent the dynamics with fewer parameters.Then

according to prior knowledge and observable data

this paper exploits model-based reinforcement learning to provide an elegant solution to the optimal exploration-exploitation tradeoff.Finally

a pointed-based Bayesian reinforcement learning approach is proposed to speed up the convergence to achieve online learning.The experimental results show that the proposed approach can approximate the underlying Bayesian reinforcement learning task well with guaranteed real-time performance.