Active Sensing for Continuous State and Action Spaces via Task-Action Entropy Minimization.

Tipakorn Greigarn, M Cenk Çavuşoğlu
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引用次数: 1

Abstract

In this paper, a new task-oriented active-sensing method is presented. Most active sensing methods choose sensing actions that minimize the uncertainty of the state according to some information-theoretic measure. While this is reasonable for most applications, minimizing state uncertainty may not be most relevant when the state information is used to perform a task. This is because the uncertainty in some subspace of the state space could have more impact on the performance of the task than the others at a given time. The active-sensing method presented in this paper takes the task into account when selecting sensing actions by minimizing the uncertainty in future task action.

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基于任务-动作熵最小化的连续状态和动作空间主动感知。
本文提出了一种新的面向任务的主动感知方法。大多数主动感知方法都是根据一定的信息论度量来选择使状态不确定性最小化的感知行为。虽然这对大多数应用程序来说是合理的,但是当使用状态信息执行任务时,最小化状态不确定性可能不是最相关的。这是因为在给定时间,状态空间的某些子空间中的不确定性可能比其他子空间对任务性能的影响更大。本文提出的主动感知方法在选择感知动作时考虑到任务本身,使未来任务动作的不确定性最小化。
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