Model-based Bayesian Reinforcement Learning with Adaptive State Aggregation
نویسندگان
چکیده
Model-based Bayesian reinforcement learning provides an elegant way of incorporating model uncertainty for trading off between exploration and exploitation. We propose an extension of modelbased Bayesian RL to continuous state spaces. The key feature of our approach is its search through the space of model structures, thus adapting not only the model parameters but also the structure itself to the problem at hand. We currently present algorithms and results for structures that are discretizations of the state space, but we hope to extend this to more powerful representations.
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