Finite State Markov

Finite state Markov models are mathematical frameworks used to represent systems transitioning between a finite number of states probabilistically. Current research focuses on improving the efficiency and accuracy of algorithms like temporal difference learning for tasks such as rare event prediction and optimal control within these models, often employing reinforcement learning techniques to optimize decision-making in complex scenarios. These advancements have significant implications for diverse fields, including traffic management (e.g., for unmanned aerial systems), communication systems (e.g., in vehicular networks), and reinforcement learning applications where accurate and efficient value function estimation is crucial.