An Efficient Algorithm for Deep Stochastic Contextual Bandits
DOI:
https://doi.org/10.1609/aaai.v35i12.17335Keywords:
Online Learning & Bandits, (Deep) Neural Network Algorithms, Reinforcement LearningAbstract
In stochastic contextual bandit (SCB) problems, an agent selects an action based on certain observed context to maximize the cumulative reward over iterations. Recently there have been a few studies using a deep neural network (DNN) to predict the expected reward for an action, and the DNN is trained by a stochastic gradient based method. However, convergence analysis has been greatly ignored to examine whether and where these methods converge. In this work, we formulate the SCB that uses a DNN reward function as a non-convex stochastic optimization problem, and design a stage-wised stochastic gradient descent algorithm to optimize the problem and determine the action policy. We prove that with high probability, the action sequence chosen by our algorithm converges to a greedy action policy respecting a local optimal reward function. Extensive experiments have been performed to demonstrate the effectiveness and efficiency of the proposed algorithm on multiple real-world datasets.Downloads
Published
2021-05-18
How to Cite
Zhu, T., Liang, G., Zhu, C., Li, H., & Bi, J. (2021). An Efficient Algorithm for Deep Stochastic Contextual Bandits. Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), 11193-11201. https://doi.org/10.1609/aaai.v35i12.17335
Issue
Section
AAAI Technical Track on Machine Learning V