An Efficient Algorithm for Deep Stochastic Contextual Bandits

• URL: http://arxiv.org/abs/2104.05613v1
• Date: Mon, 12 Apr 2021 16:34:43 GMT
• Title: An Efficient Algorithm for Deep Stochastic Contextual Bandits
• Authors: Tan Zhu, Guannan Liang, Chunjiang Zhu, Haining Li, Jinbo Bi
• Abstract summary: In contextual bandit problems, an agent selects an action based on certain observed context to maximize the 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 is trained by a gradient based method.
• Score: 10.298368632706817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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-wise stochastic gradient descent algorithm to optimize the problem and determine the action policy. We prove that with high probability, the action sequence chosen by this 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.

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