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Bayesian methods suffer from the problem of how to specify prior beliefs. Oneinteresting idea is to consider worst-case priors. This requires solving astochastic zero-sum game. In this paper, we extend well-known results frombandit theory in order to discover minimax-Bayes policies and discuss when theyare practical.

Generalised Entropy MDPs and Minimax Regret