£69.99
Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains—efficiently processing new information by adjusting the control strategies directly or indirectly.
Modern Trends in Controlled Stochastic Processes:
Markov Chains: Theory and Applications
Optimization and Games for Controllable Markov Chains
Constrained Markov Decision Processes
Learning Automata
Adaptive and Learning-Based Control of Safety-Critical Systems
Self-Learning Optimal Control of Nonlinear Systems
Learning Algorithms
Learning Decision Sequences For Repetitive Processes-Selected Algorithms
Optimization for Learning and Control
Reinforcement Learning
Learning Automata and Their Applications to Intelligent Systems