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Concurrent learning of task and attention control in the decision space

机译：决策空间中任务和注意力控制的并发学习

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摘要

Learning attention control is a real need specifically when a robot tries to learn a sequential decision-making-type task. This is even more critical when learning directly in the perceptual space is not feasible mainly due to the high dimensionality thus non-homogeneity. Therefore, two learning problems are raised to be solved at the same time. In this paper, a novel approach with three learning phases is proposed to facilitate learning of these two coupled problems: 1) learning how to divide attention among multiple dimensions of robots perceptual space and also how to shift it efficiently inside one modality from one spatial part to another and 2) learning the main task. The main task is considered ldquodriving in a simulated road using a miniature mobile robotrdquo in order to demonstrate the necessity of attention control. An important new feature of the proposed learning method is that the attention is learned in the decision space rather than the original perceptual space and this brings some discussed advantages. Obtained results justify practicability and usefulness of learning attention control in the proposed alternate space.
机译：学习注意力控制是一个真正的需要，特别是当机器人试图学习序贯决策类型任务时。当直接在感知空间中直接学习时，这通常是更重要的是主要是由于较高的维度不均匀。因此，提高了两个学习问题以同时解决。在本文中，提出了一种具有三个学习阶段的新方法，以便于学习这两个耦合问题：1）学习如何在机器人感知空间的多个维度之间进行注意，以及如何从一个空间部分将其有效地在一个模态内移动另一个和2）学习主要任务。主要任务在使用微型移动Robotrdquo的模拟道路中被认为是Ldquodriving，以证明注意力控制的必要性。建议的学习方法的一个重要新特征是在决策空间中学习的注意力而不是原始的感知空间，这带来了一些讨论的优势。获得的结果是在建议的交替空间中学习注意力控制的可行性和有用性。

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