On self-optimizing control of Markov processes
Petr Mandl (1985)
Banach Center Publications
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Displaying similar documents to “On risk sensitive control of regular step Markov processes”
On self-optimizing control of Markov processes
Estimates for perturbations of general discounted Markov control chains
Raúl Montes-de-Oca, Alexander Sakhanenko, Francisco Salem-Silva (2003)
Applicationes Mathematicae
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We extend previous results of the same authors ([11]) on the effects of perturbation in the transition probability of a Markov cost chain for discounted Markov control processes. Supposing valid, for each stationary policy, conditions of Lyapunov and Harris type, we get upper bounds for the index of perturbations, defined as the difference of the total expected discounted costs for the original Markov control process and the perturbed one. We present examples that satisfy our conditions. ...
Ergodic control of partially observed Markov processes with equivalent transition probabilities
Łukasz Stettner (1993)
Applicationes Mathematicae
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Optimal control with long run average cost functional of a partially observed Markov process is considered. Under the assumption that the transition probabilities are equivalent, the existence of the solution to the Bellman equation is shown, with the use of which optimal strategies are constructed.
Semi-Markov control processes with non-compact action spaces and discontinuous costs
Anna Jaśkiewicz (2009)
Applicationes Mathematicae
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We establish the average cost optimality equation and show the existence of an (ε-)optimal stationary policy for semi-Markov control processes without compactness and continuity assumptions. The only condition we impose on the model is the V-geometric ergodicity of the embedded Markov chain governed by a stationary policy.
Deterministic optimal policies for Markov control processes with pathwise constraints
Armando F. Mendoza-Pérez, Onésimo Hernández-Lerma (2012)
Applicationes Mathematicae
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This paper deals with discrete-time Markov control processes in Borel spaces with unbounded rewards. Under suitable hypotheses, we show that a randomized stationary policy is optimal for a certain expected constrained problem (ECP) if and only if it is optimal for the corresponding pathwise constrained problem (pathwise CP). Moreover, we show that a certain parametric family of unconstrained optimality equations yields convergence properties that lead to an approximation scheme which...
Adaptive control for discrete-time Markov processes with unbounded costs: Discounted criterion
Evgueni I. Gordienko, J. Adolfo Minjárez-Sosa (1998)
Kybernetika
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We study the adaptive control problem for discrete-time Markov control processes with Borel state and action spaces and possibly unbounded one-stage costs. The processes are given by recurrent equations with i.i.d. -valued random vectors whose density is unknown. Assuming observability of we propose the procedure of statistical estimation of that allows us to prove discounted asymptotic optimality of two types of adaptive policies used early for the processes with bounded...
Time-discretization for controlled Markov processes. I. General approximation results
Nico M. van Dijk, Arie Hordijk (1996)
Kybernetika
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Discounted Markov control processes induced by deterministic systems
Hugo Cruz-Suárez, Raúl Montes-de-Oca (2006)
Kybernetika
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This paper deals with Markov Control Processes (MCPs) on Euclidean spaces with an infinite horizon and a discounted total cost. Firstly, MCPs which result from the deterministic controlled systems will be analyzed. For such MCPs, conditions that permit to establish the equation known in the literature of Economy as Euler’s Equation (EE) will be given. There will be also presented an example of a Markov Control Process with deterministic controlled system where, to obtain the optimal...