Attentional Action Selection Using Reinforcement Learning

Abstract

Reinforcement learning is typically used to model and optimize action selection strategies, in this work we deploy it to optimize attentional allocation strategies while action selection is obtained as a side effect. We present a reinforcement learning approach to attentional allocation and action selection in a behavior-based robotic systems. We detail our attentional allocation mechanisms describing the reinforcement learning problem and analysing its performance in a survival domain.