Decision making in noisy and changing environments requires a fine balance between
exploiting knowledge about good courses of action and exploring the environment in order …

Adaptive and sequential experiment design is a well-studied area in numerous domains. We
survey and synthesize the work of the online statistical learning paradigm referred to as multi …

Abstract Multi-Armed Bandit (MAB) techniques have been successfully applied to many
classes of sequential decision problems in the past decades. However, non-stationary …

The importance of multi-armed bandit (MAB) problems is on the rise due to their recent
application in a large variety of areas such as online advertising, news article selection …

Experimental researchers in political science frequently face the problem of inferring which
of several treatment arms is most effective. They may also seek to estimate mean outcomes …

Kinodynamic motion planners allow robots to perform complex manipulation tasks under
dynamics constraints or with black-box models. However, they struggle to find high-quality …

We present a novel approach to deformable object manipulation that does not rely on highly
accurate modeling. The key contribution of this paper is to formulate the task as a …

Although the field of learning automata (LA) has made significant progress in the past four
decades, the LA-based methods to tackle problems involving environments with a large …

We consider the sequential resource allocation problem under the multi-armed bandit model
in the non-stationary stochastic environment. Motivated by many real applications, where …

Motor rehabilitation is used to improve motor control skills to improve the patient's quality of
life. Regular adjustments based on the effect of therapy are necessary, but this can be time …