AI engineering glossary · Reinforcement learning
What is contextual bandit?
Contextual bandit: A bandit problem where each round begins with side information, such as user features, and the best option depends on it. Choices do not affect later situations, which makes it simpler than full sequential decision-making, and widely used for personalisation.
Contextual bandit: an example
A news app picks one of 5 headlines for each user using age band and city; the reward is 1 if clicked.
Related terms
More Reinforcement learning terms
- Continuing task
- Count-based exploration
- Credit assignment
- Curiosity-driven exploration
- DAgger
- Deadly triad
- Deep deterministic policy gradient
- Deep Q-network
Where you learn it
Contextual bandit is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.