AI engineering glossary · Reinforcement learning
What is POMDP (Partially observable Markov decision process)?
Partially observable Markov decision process (also called POMDP): An extension of the standard sequential decision model in which the learner cannot see the true situation, only noisy observations related to it. It must keep a belief or memory of past observations, for example using a recurrent network, to act well.
Partially observable Markov decision process: an example
A poker player sees only their own cards and the bets; the opponents' cards are hidden, so play depends on a belief about them.
Related terms
More Reinforcement learning terms
- Policy evaluation
- Policy gradient theorem
- Policy improvement
- Policy iteration
- Potential-based reward shaping
- Prioritised experience replay
- Q-learning
- Q-table
Where you learn it
Partially observable 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.