# A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning Source: [A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:59:47.071Z Authors: Hossein K. Mousavi, Guangyi Liu, Weihang Yuan, Martin Tak\\'a\\v{c}, H\\'ector Mu\\~noz-Avila, Nader Motee Published: 2019-09-20T19:52:41.000Z ## Abstract We propose a planning and perception mechanism for a robot (agent), that can only observe the underlying environment partially, in order to solve an image classification problem. A three-layer architecture is suggested that consists of a meta-layer that decides the intermediate goals, an action-layer that selects local actions as the agent navigates towards a goal, and a classification-layer that evaluates the reward and makes a prediction. We design and implement these layers using deep reinforcement learning. A generalized policy gradient algorithm is utilized to learn the parameters of these layers to maximize the expected reward. Our proposed methodology is tested on the MNIST dataset of handwritten digits, which provides us with a level of explainability while interpreting the agent's intermediate goals and course of action. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()