AI engineering glossary · Reinforcement learning
What is MDP (markov decision process)?
Markov decision process (also called MDP): A mathematical model of sequential decision-making with a set of situations, a set of choices, transition probabilities, a score function and a discount factor. Its key assumption is that the next situation depends only on the current one and the choice made. Most learning methods assume it.
Markov decision process: an example
A 4x4 grid world with 16 states, 4 moves, slippery floor (80 percent chance of moving as intended) and goal reward +1 is a small MDP.
Related terms
More Reinforcement learning terms
- Maximum entropy reinforcement learning
- Model predictive control
- Model-based
- Model-free
- Monte Carlo methods (reinforcement learning)
- Monte Carlo tree search
- Multi-agent reinforcement learning
- Multi-armed bandit
Where you learn it
Markov decision process is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.