  In a recent work, Laforgue et al. introduce the model of last switch
dependent (LSD) bandits, in an attempt to capture nonstationary phenomena
induced by the interaction between the player and the environment. Examples
include satiation, where consecutive plays of the same action lead to decreased
performance, or deprivation, where the payoff of an action increases after an
interval of inactivity. In this work, we take a step towards understanding the
approximability of planning LSD bandits, namely, the (NP-hard) problem of
computing an optimal arm-pulling strategy under complete knowledge of the
model. In particular, we design the first efficient constant approximation
algorithm for the problem and show that, under a natural monotonicity
assumption on the payoffs, its approximation guarantee (almost) matches the
state-of-the-art for the special and well-studied class of recharging bandits
(also known as delay-dependent). In this attempt, we develop new tools and
insights for this class of problems, including a novel higher-dimensional
relaxation and the technique of mirroring the evolution of virtual states. We
believe that these novel elements could potentially be used for approaching
richer classes of action-induced nonstationary bandits (e.g., special instances
of restless bandits). In the case where the model parameters are initially
unknown, we develop an online learning adaptation of our algorithm for which we
provide sublinear regret guarantees against its full-information counterpart.
