Fully Observable Markov Decision Processes

Fully Observable Markov Decision Processes (FOMDPs) are a mathematical framework used in artificial intelligence, operations research, and robotics to model sequential decision-making problems under uncertainty. They consist of states, actions, transitions between states with probabilistic outcomes, and rewards, with the key feature that the agent has complete knowledge of the current state at all times. This framework enables the computation of optimal policies that maximize cumulative rewards over time, often using algorithms like value iteration or policy iteration.

Also known as: FOMDP, Markov Decision Process, MDP, Fully Observable MDP, Deterministic Observation MDP

Why learn Fully Observable Markov Decision Processes?

Developers should learn FOMDPs when working on reinforcement learning, autonomous systems, or optimization problems where decisions must be made in dynamic environments with known states, such as in robotics path planning, game AI, or resource management. It provides a foundational model for solving problems where uncertainty in outcomes exists but the state is fully observable, allowing for efficient planning and learning algorithms to derive optimal strategies.

Learning Resources

• book: Reinforcement Learning: An Introduction - Sutton & Barto
• docs: Markov Decision Processes - Wikipedia
• course: CS188: Artificial Intelligence - UC Berkeley
• video: Reinforcement Learning Course by David Silver
• tutorial: Markov Decision Processes Tutorial on Towards Data Science