Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits. (arXiv:2002.00287v3 [cs.LG] UPDATED)

We consider an adversarial variant of the classic $K$-armed linear contextual
bandit problem where the sequence of loss functions associated with each arm
are allowed to change without restriction over time. Under the assumption that
the $d$-dimensional contexts are generated i.i.d.~at random from a known
distributions, we develop computationally efficient algorithms based on the
classic Exp3 algorithm. Our first algorithm, RealLinExp3, is shown to achieve a
regret guarantee of $\widetilde{O}(\sqrt{KdT})$ over $T$ rounds, which matches
the best available bound for …

algorithms arxiv linear