= Markov decision process
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A Markov decision process specifies states, admissible actions, action-dependent <transition probabilities> and rewards. A policy chooses actions from observed information; a time-homogeneous problem uses transition and reward rules independent of the date. A <Bellman equation> optimizes current reward plus the conditional value of subsequent decisions. For nonnegative rewards, finite-horizon values increase as the horizon grows, while identification of their limit with the infinite-horizon value requires an approximation argument for admissible policies.
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