Distributionally Robust Markov Decision Processes

NeurIPS 2010 Huan XuShie Mannor

We consider Markov decision processes where the values of the parameters are uncertain. This uncertainty is described by a sequence of nested sets (that is, each set contains the previous one), each of which corresponds to a probabilistic guarantee for a different confidence level so that a set of admissible probability distributions of the unknown parameters is specified... (read more)

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