Hyper-parameter Tuning for the Contextual Bandit
arXiv:2005.02209
Abstract
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.
arXiv admin note: text overlap with arXiv:1705.03821
References in corpus (6)
- A Contextual-Bandit Approach to Personalized News Article Recommendation
- Analysis of Thompson Sampling for the multi-armed bandit problem
- Thompson Sampling for Contextual Bandits with Linear Payoffs
- Meta-Learning for Contextual Bandit Exploration
- A Neural Networks Committee for the Contextual Bandit Problem
- Bandit Models of Human Behavior: Reward Processing in Mental Disorders
Cited by in corpus (5)
- Contextual Bandit with Missing Rewards
- Spectral Clustering using Eigenspectrum Shape Based Nystrom Sampling
- Computing the Dirichlet-Multinomial Log-Likelihood Function
- Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
- Etat de l'art sur l'application des bandits multi-bras