# Image Classification by Reinforcement Learning with Two-State Q-Learning

arXiv: https://arxiv.org/abs/2007.01298 · HTML: https://mlsanity.com/paper/2007.01298/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery](https://mlsanity.com/paper/2308.13801/) (2023)
- [Reinforcement Learning with Generative Models for Compact Support Sets](https://mlsanity.com/paper/2404.16300/) (2024)
- [Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness](https://mlsanity.com/paper/2406.06792/) (2024)

## Abstract (arXiv)

In this paper, a simple and efficient Hybrid Classifier is presented which is based on deep learning and reinforcement learning. Here, Q-Learning has been used with two states and 'two or three' actions. Other techniques found in the literature use feature map extracted from Convolutional Neural Networks and use these in the Q-states along with past history. This leads to technical difficulties in these approaches because the number of states is high due to large dimensions of the feature map. Because the proposed technique uses only two Q-states it is straightforward and consequently has much lesser number of optimization parameters, and thus also has a simple reward function. Also, the proposed technique uses novel actions for processing images as compared to other techniques found in literature. The performance of the proposed technique is compared with other recent algorithms like ResNet50, InceptionV3, etc. on popular databases including ImageNet, Cats and Dogs Dataset, and Caltech-101 Dataset. The proposed approach outperforms others techniques on all the datasets used.