Markov Decision Processes

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision-making problems under uncertainty, where outcomes are partly random and partly under the control of a decision-maker. They are widely used in reinforcement learning, operations research, and artificial intelligence to formalize problems involving states, actions, transitions, and rewards. MDPs provide a foundation for algorithms that find optimal policies to maximize cumulative rewards over time.

Also known as: MDPs, Markov Decision Process, Markov Decision Models, MDP, Markovian Decision Processes

Why learn Markov Decision Processes?

Developers should learn MDPs when working on reinforcement learning projects, robotics, game AI, or any system requiring automated decision-making in stochastic environments. They are essential for building intelligent agents that learn from interactions, such as in recommendation systems, autonomous vehicles, or resource management, as they enable the formulation and solution of optimization problems with probabilistic outcomes.

Learning Resources

• book: Reinforcement Learning: An Introduction - Sutton & Barto
• docs: Markov Decision Processes on Wikipedia
• course: CS234: Reinforcement Learning - Stanford University
• video: Markov Decision Processes Tutorial by David Silver
• tutorial: OpenAI Spinning Up in Deep RL - MDPs Section