Markov Decision Processes

Markov Decision Processes (MDPs) are constructed via the addition of an additional layer of “actions” to a standard Markov model. They are useful to the development of Q-learning algorithms for reinforcement learning. Applications include game theory, recommender systems, robotics, automated control, operations research, information theory, multi-agent systems, swarm intelligence, and genetic algorithms.