Markov Decision Processes

AI, ML, Reinforcement Learning, Markov Decision Process, MDP, Bellman Equation

Markov Decision Processes #

A Markov Decision Process (MDP) is the mathematical framework used to describe sequential decision-making in reinforcement learning. It brings together the agent, environment, states, actions, transition dynamics, rewards, returns, policies, and value functions in one model.

A basic bandit asks which action is best in a recurring situation. An MDP goes further: the action taken now can change the next state, which changes the decisions and rewards that become possible later.