Acting and Bayesian reinforcement structure learning of partially observable environment

Abstract: This article shows how to learn both the structure and the parameters of partially observable en-vironment simultaneously while also online performing near-optimal sequence of actions taking into account exploration-exploitation tradeoff. It combines two re-sults of recent research: The former extends model-based Bayesian reinforcement learning of fully observable envi-ronment to bigger domains by learning the structure. The latter shows how a known structure can be exploited to model-based Bayesian reinforcement learning of partially observable domains. This article shows that merging both approaches is possible without too excessive increase in computational complexity.