Stochastic Conservative Contextual Linear Bandits

Lin, Jiabin
Lee, Xian Yeow
Jubery, Talukder
Moothedath, Shana
Sarkar, Soumik
Ganapathysubramanian, Baskar
Major Professor
Committee Member
Journal Title
Journal ISSN
Volume Title
Research Projects
Organizational Units
Mechanical Engineering
Organizational Unit
Plant Sciences Institute
Organizational Unit
Journal Issue
Mechanical EngineeringElectrical and Computer EngineeringPlant Sciences Institute
Many physical systems have underlying safety considerations that require that the strategy deployed ensures the satisfaction of a set of constraints. Further, often we have only partial information on the state of the system. We study the problem of safe real-time decision making under uncertainty. In this paper, we formulate a conservative stochastic contextual bandit formulation for real-time decision making when an adversary chooses a distribution on the set of possible contexts and the learner is subject to certain safety/performance constraints. The learner observes only the context distribution and the exact context is unknown, and the goal is to develop an algorithm that selects a sequence of optimal actions to maximize the cumulative reward without violating the safety constraints at any time step. By leveraging the UCB algorithm for this setting, we propose a conservative linear UCB algorithm for stochastic bandits with context distribution. We prove an upper bound on the regret of the algorithm and show that it can be decomposed into three terms: (i) an upper bound for the regret of the standard linear UCB algorithm, (ii) a constant term (independent of time horizon) that accounts for the loss of being conservative in order to satisfy the safety constraint, and (ii) a constant term (independent of time horizon) that accounts for the loss for the contexts being unknown and only the distribution being known. To validate the performance of our approach we perform extensive simulations on synthetic data and on real-world maize data collected through the Genomes to Fields (G2F) initiative.
This is a pre-print of the article Lin, Jiabin, Xian Yeow Lee, Talukder Jubery, Shana Moothedath, Soumik Sarkar, and Baskar Ganapathysubramanian. "Stochastic Conservative Contextual Linear Bandits." arXiv preprint arXiv:2203.15629 (2022). DOI: 10.48550/arXiv.2203.15629. Copyright 2022 The Author(s). Posted with permission.