Dynamic Multi-Armed Bandit with Covariates
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Optimistic Bayesian sampling in contextual-bandit problems
The Journal of Machine Learning Research
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We evaluate the performance of a number of action-selection methods on the multi-armed bandit problem with covariates. We resort to simulations because our primary concern is the speed with which the different methods identify the optimal policy, and not their asymptotic behaviour. The experimental results show that the performance of the ε-greedy methods is robust, while the interval estimation strategies achieve the fastest learning of the optimal policy. We propose a metric to quantify the difficulty of a multi-armed bandit problem with covariates and show that there is a trade-off between the satisfaction of the different performance measures.