  This paper introduces an interpretable contextual bandit algorithm using
Tsetlin Machines, which solves complex pattern recognition tasks using
propositional logic. The proposed bandit learning algorithm relies on
straightforward bit manipulation, thus simplifying computation and
interpretation. We then present a mechanism for performing Thompson sampling
with Tsetlin Machine, given its non-parametric nature. Our empirical analysis
shows that Tsetlin Machine as a base contextual bandit learner outperforms
other popular base learners on eight out of nine datasets. We further analyze
the interpretability of our learner, investigating how arms are selected based
on propositional expressions that model the context.
