Adapting multi-armed bandits policies to contextual bandits scenarios
arXiv:1811.04383
Abstract
This work explores adaptations of successful multi-armed bandits policies to the online contextual bandits scenario with binary rewards using binary classification algorithms such as logistic regression as black-box oracles. Some of these adaptations are achieved through bootstrapping or approximate bootstrapping, while others rely on other forms of randomness, resulting in more scalable approaches than previous works, and the ability to work with any type of classification algorithm. In particular, the Adaptive-Greedy algorithm shows a lot of promise, in many cases achieving better performance than upper confidence bound and Thompson sampling strategies, at the expense of more hyperparameters to tune.
References in corpus (8)
- A Contextual-Bandit Approach to Personalized News Article Recommendation
- Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
- Contextual Bandits with Similarity Information
- Efficient Optimal Learning for Contextual Bandits
- A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit
- A Contextual Bandit Bake-off
- Practical Contextual Bandits with Regression Oracles
- Thompson sampling with the online bootstrap