Sensitivity of constrained Markov decision processes
Annals of Operations Research
Adaptive control of constrained Markov chains: criteria and policies
Annals of Operations Research
Discrete-time controlled Markov processes with average cost criterion: a survey
SIAM Journal on Control and Optimization
Learning automata: theory and applications
Learning automata: theory and applications
Constrained discounted dynamic programming
Mathematics of Operations Research
Asymptotically Efficient Adaptive Choice of Control Laws inControlled Markov Chains
SIAM Journal on Control and Optimization
Optimal adaptive policies for Markov decision processes
Mathematics of Operations Research
Adaptive Markov Control Processes
Adaptive Markov Control Processes
Dynamic Programming and Stochastic Control
Dynamic Programming and Stochastic Control
Random Iterative Models
Learning Automata and Stochastic Optimization
Learning Automata and Stochastic Optimization
Finite State Markovian Decision Processes
Finite State Markovian Decision Processes
Stochastic approximation of constrained systems with system and constraint noise
Automatica (Journal of IFAC)
An adaptive automaton controller for discrete-time markov processes
Automatica (Journal of IFAC)
On constrained Markov decision processes
Operations Research Letters
| Hi-index | 22.15 |
An adaptive control algorithm is presented for constrained finite controlled Markov chains with unknown transition probabilities. A finite set of algebraic constraints has been considered. The Lagrange multipliers approach is used to solve this constrained optimization problem. This scheme is such that at each time n estimates the control policy on the basis on Bush-Mosteller scheme which is related to stochastic approximation procedures. We present the asymptotic properties (convergence and order of convergence rate) of the algorithm. They follow from the law of dependent large numbers, martingales theory and Lyapunov function analysis approaches.