AI engineering glossary · Reinforcement learning
What is Double Q-learning?
Double Q-learning: A variant of Q-learning that keeps two independent value estimates: one picks the best next choice and the other evaluates it. This decoupling removes most of the upward bias that a single maximum creates.
Double Q-learning: an example
Estimator A picks argmax over a' and estimator B supplies its value in the target.
Related terms
More Reinforcement learning terms
- Dueling network
- Dyna
- Dynamic programming (reinforcement learning)
- Eligibility trace
- Entropy regularisation
- Environment
- Episode
- Episodic task
Where you learn it
Double Q-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.