Markov Decision Processes
Markov Decision Processes are mathematical models used to describe decision-making processes in situations where outcomes are partially random and partially under the control of a decision-maker. They consist of states, actions, transition probabilities, and rewards, with the goal being to find the optimal policy that maximizes the expected cumulative reward. Markov Decision Processes are widely used in a variety of fields, including artificial intelligence, operations research, economics, and engineering.