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In this note we focus attention on identifying optimal policies and on elimination suboptimal policies minimizing optimality criteria in discrete-time Markov decision processes with finite state space and compact action set. We present unified approach to value iteration algorithms that enables to generate lower and upper bounds on optimal values, as well as on the current policy. Using the modified value iterations it is possible to eliminate suboptimal actions and to identify an optimal policy...
We consider discrete-time Markov control processes on Borel spaces and infinite-horizon undiscounted cost criteria which are sensitive to the growth rate of finite-horizon costs. These criteria include, at one extreme, the grossly underselective average cost
Markov Decision Processes (MDPs) are a classical framework for
stochastic sequential decision problems, based on an enumerated state
space representation. More compact and structured representations have
been proposed: factorization techniques use state variables
representations, while decomposition techniques are based on a
partition of the state space into sub-regions and take advantage of
the resulting structure of the state transition graph. We use a family
of probabilistic exploration-like...
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