Abstract:

Semi-Markov decision processes are used to formulate many control problems and also play a key role in hierarchical reinforcement learning. In this chapter we show how to translate the decision making problem into a form that can instead be solved by inference and learning techniques. In particular, we will establish a formal connection between planning in semi-Markov decision processes and inference in probabilistic graphical models, then build on this connection to develop an expectation ma...

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