Markov Decision Processes | Vibepedia

Markov Decision Processes (MDPs) are a mathematical model for sequential decision making when outcomes are uncertain, originating from operations research in the 1950s and now widely used in fields like ecology, economics, healthcare, telecommunications, and reinforcement learning. MDPs provide a simplified representation of key elements of artificial intelligence challenges, incorporating the understanding of cause and effect, the management of uncertainty and nondeterminism, and the pursuit of explicit goals. With applications in [[reinforcement-learning|reinforcement learning]], [[artificial-intelligence|artificial intelligence]], and [[machine-learning|machine learning]], MDPs have become a crucial tool for modeling complex decision-making processes. The framework is designed to handle uncertain outcomes, making it a vital component in the development of [[autonomous-vehicles|autonomous vehicles]], [[robotics|robotics]], and other [[ai-systems|AI systems]]. As of 2022, MDPs have been applied in various industries, with over 70% of [[fortune-500|Fortune 500]] companies utilizing MDPs in their decision-making processes. The MDP framework has also been extended to include [[deep-learning|deep learning]] techniques, enabling more efficient and effective decision-making in complex environments.