AI engineering glossary · Reinforcement learning
What is dynamic programming (reinforcement learning)?
Dynamic programming (reinforcement learning): The family of planning methods that compute values and best decision rules by breaking the problem into overlapping sub-problems using the Bellman equations. They need a full model of the transition probabilities and scores, and cost grows quickly with the number of situations.
Dynamic programming (reinforcement learning): an example
Solving a 100-state inventory problem exactly by sweeping all states repeatedly.
Related terms
More Reinforcement learning terms
- Eligibility trace
- Entropy regularisation
- Environment
- Episode
- Episodic task
- Epsilon-greedy
- Experience replay
- Exploitation
Where you learn it
Dynamic programming (reinforcement learning) is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.