# A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1909.09705 · HTML: https://mlsanity.com/paper/1909.09705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input](https://mlsanity.com/paper/2203.05403/) (2022)

## Abstract (arXiv)

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.