Restless Bandit Problem with Rewards Generated by a Linear Gaussian Dynamical System

The stochastic multi-armed bandit problem studies decision-making under uncertainty. In the problem, the learner interacts with an environment by choosing an action at each round, where a round is an instance of an interaction. In response, the environment reveals a reward, which is sampled from a stochastic process, to the learner. The goal of the learner is to maximize cumulative reward. A specific variation of the stochastic multi-armed bandit problem is the restless bandit, where the reward for each action is sampled from a Markov chain. The restless bandit with a discrete state-space is a well-studied problem, but to the best of our knowledge, not many results exist for the continuous state-space version which has many applications such as hyperparameter optimization. In this work, we tackle the restless bandit with continuous state-space by assuming the rewards are the inner product of an action vector and a state vector generated by a linear Gaussian dynamical system. To predict the reward for each action, we propose a method that takes a linear combination of previously observed rewards for predicting each action's next reward. We show that, regardless of the sequence of previous actions chosen, the reward sampled for any previously chosen action can be used for predicting another action's future reward, i. e. the reward sampled for action 1 at round t-1 can be used for predicting the reward for action 2 at round t. This is accomplished by designing a modified Kalman filter with a matrix representation that can be learned for reward prediction. Numerical evaluations are carried out on a set of linear Gaussian dynamical systems.