We consider a variant of the multi-armed bandit model, which we call multi-armed bandit problem with known trend, where the gambler knows the shape of the reward function of each arm but not its distribution. This new problem is motivated by different online problems like active learning, music and interface recommendation applications, where when an arm is sampled by the model the received reward change according to a known trend. By adapting the standard multi-armed bandit algorithm UCB1 to take advantage of this setting, we propose the new algorithm named A-UCB that assumes a stochastic model. We provide upper bounds of the regret which compare favourably with the ones of UCB1. We also confirm that experimentally with different simulations
In @cite_11 author study specific classes of drifting restless bandits selected for their relevance to modelling an online website optimization process. The contribution was a feasible weighted least squares technique capable of utilizing contextual arm parameters while considering the parameter space drifting non-stationary within reasonable bounds.
@cite_11
2293196265
Restless bandits model the exploration vs. exploitation trade-off in a changing (non-stationary) world. Restless bandits have been studied in both the context of continuously-changing (drifting) and change-point (sudden) restlessness. In this work, we study specific classes of drifting restless bandits selected for their relevance to modelling an online website optimization process. The contribution in this work is a simple, feasible weighted least squares technique capable of utilizing contextual arm parameters while considering the parameter space drifting non-stationary within reasonable bounds. We produce a reference implementation, then evaluate and compare its performance in several different true world states, finding experimentally that performance is robust to time drifting factors similar to those seen in many real world cases.