Markov Decision Processes: Discrete Stochastic Dynamic Programming

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Reinforcement learning in robotics: A survey (2013, background): “It can be shown (Puterman, 1994) that such policies that map states (even deterministically) to actions are sufficient to ensure optimality in this setting: a policy needs neither to remember previous states visited, actions taken, or the particular time step.”
An MDP-Based Recommender System (2002, positive): “e optimized taking into account the sequential process involved and the op timization criteria suitable for the Recommender system. Thus, we suggest the use of Markov Decision Processes (MDP) (e.g., Puterman, 1994), a well known stochastic model of sequential decisions. With this view in mind, a mor”
Exact finite approximations of average-cost countable Markov decision processes (2007, positive): “rees with σ on corresponding states of Z and Z 0. The theorem assumes explicitly that there exists a stationary Markov optimal policy. This holds for most applications: for conditions see for example [P] and references therein. 4 Extensions First, note that the requirement that the cycle cost is ﬁni”