Picture a warehouse robot. Its state is where it is and what it carries. Its actions are moves like “go left” or “pick up box”. Each action changes the state, sometimes unpredictably if the floor is crowded, and earns a reward such as +10 for a delivered box or -1 for every wasted second. Writing the problem down this way is an MDP. A policy is the robot’s rule for which action to take in each state.
An MDP is a Markov chain with choices and rewards added. The “Markov” part means the next state depends only on the current state and action.
Why it matters: Sutton and Barto’s standard textbook frames reinforcement learning problems as MDPs, and methods such as Q-learning are ways of solving them when the probabilities are unknown. When guests on the show talk about training models with RL, the reward-and-policy vocabulary they use comes from this framework. That connection is editorial synthesis; no episode has used the term “Markov decision process” directly.
Sources
- Sutton and Barto, Reinforcement Learning: An Introduction (2nd ed.) — Authors' free edition; Chapter 3 defines finite MDPs, states, actions, rewards, policies and returns, checked October 24, 2026