The paper introduces a linear bandit environment where the reward is the output of a known Linear Gaussian Dynamical System (LGDS). In this environment, we address the fundamental challenge of balancing exploration -- gathering information about the environment -- and exploitation -- selecting to the action with the highest predicted reward. We propose two algorithms, Kalman filter Upper Confidence Bound (Kalman-UCB) and Information filter Directed Exploration Action-selection (IDEA). Kalman-UCB uses the principle of optimism in the face of uncertainty. IDEA selects actions that maximize the combination of the predicted reward and a term that quantifies how much an action minimizes the error of the Kalman filter state prediction, which depends on the LGDS property called observability. IDEA is motivated by applications such as hyperparameter optimization in machine learning. A major problem encountered in hyperparameter optimization is the large action spaces, which hinder the performance of methods inspired by principle of optimism in the face of uncertainty as they need to explore each action to lower reward prediction uncertainty. To predict if either Kalman-UCB or IDEA will perform better, a metric based on the LGDS properties is provided. This metric is validated with numerical results across a variety of randomly generated environments.
Gornet et al. (Wed,) studied this question.