Infinite Horizon

Infinite-horizon problems in Markov Decision Processes (MDPs) focus on optimizing long-term rewards or costs in dynamic systems with potentially unbounded time horizons. Current research emphasizes developing efficient algorithms, such as policy gradient methods, Thompson sampling, and value iteration variants, often incorporating techniques like function approximation and regularization to handle large or continuous state and action spaces. These advancements address challenges in areas like reinforcement learning, control theory, and operations research, improving the ability to model and solve complex sequential decision-making problems in various applications. The resulting improvements in algorithm efficiency and theoretical understanding have significant implications for fields ranging from robotics and healthcare to resource management and finance.