AI engineering glossary · Reinforcement learning
What is greedy policy?
Greedy policy: A decision rule that always picks the choice with the highest current estimated value. It is optimal only if the estimates are correct, and it never explores, so it can lock into early mistakes.
Greedy policy: an example
If Q estimates are 2.1, 3.4 and 3.3, the greedy choice is the second one every time.
Related terms
More Reinforcement learning terms
- Hierarchical reinforcement learning
- Hindsight experience replay
- Horizon
- Imitation learning
- Importance sampling ratio
- Intrinsic motivation
- Inverse reinforcement learning
- Markov decision process
Where you learn it
Greedy policy is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.