Hyper-parameter Tuning for the Contextual Bandit
This work addresses the need for automated hyperparameter tuning in bandit algorithms, which is an incremental improvement over manual tuning methods.
The paper tackles the problem of automating exploration parameter tuning in contextual bandits with linear rewards, proposing two algorithms that learn optimal exploration parameters online based on context and immediate rewards.
We study here the problem of learning the exploration exploitation trade-off in the contextual bandit problem with linear reward function setting. In the traditional algorithms that solve the contextual bandit problem, the exploration is a parameter that is tuned by the user. However, our proposed algorithm learn to choose the right exploration parameters in an online manner based on the observed context, and the immediate reward received for the chosen action. We have presented here two algorithms that uses a bandit to find the optimal exploration of the contextual bandit algorithm, which we hope is the first step toward the automation of the multi-armed bandit algorithm.