Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Deterministic Approximation for Stochastic Control Problems
SIAM Journal on Control and Optimization
Rates of Convergence for Approximation Schemes in Optimal Control
SIAM Journal on Control and Optimization
Adaptive Markov Control Processes
Adaptive Markov Control Processes
SIAM Journal on Control and Optimization
The problem of robot random motion tracking learning algorithms
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Quad tree decomposition of fused image of sunspots for classifying the trajectories
ICAI'06 Proceedings of the 7th WSEAS International Conference on Automation & Information
TELE-INFO'06 Proceedings of the 5th WSEAS international conference on Telecommunications and informatics
Application of genetic algorithm for designing cellular manufacturing system incrementally
WSEAS Transactions on Computers
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This paper deals with Markov Decision Processes (MDPs) on Borel spaces with an infinite horizon and a discounted total cost. It will be considered a stochastic optimal control problem which arises by perturbing the transition law of a deterministic control problem, through an additive random noise term with coefficient epsilon. In the paper, we will analyze the behavior of the optimal solution (optimal value function and optimal policy) of the stochastic system, when the coefficient epsilon goes to zero. Specifically, conditions given in the paper guarantee the uniform on compact sets convergence of both the optimal value function and the optimal policy of the stochastic system to the optimal value function and the optimal policy of the deterministic one, when epsilon goes to zero, respectively. Finally, two examples which illustrate the developed theory are presented.