# Object Detection with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2208.04511 · HTML: https://mlsanity.com/paper/2208.04511/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 4 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Active Object Localization with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06015/) (2015)
- [Hierarchical Object Detection with Deep Reinforcement Learning](https://mlsanity.com/paper/1611.03718/) (2016)

## Built on by (most cited first, 2 of 2)

- [Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object Detection](https://mlsanity.com/paper/2408.06803/) (2024)
- [GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking](https://mlsanity.com/paper/2607.15699/) (2026)

## Abstract (arXiv)

Object localization has been a crucial task in computer vision field. Methods of localizing objects in an image have been proposed based on the features of the attended pixels. Recently researchers have proposed methods to formulate object localization as a dynamic decision process, which can be solved by a reinforcement learning approach. In this project, we implement a novel active object localization algorithm based on deep reinforcement learning. We compare two different action settings for this MDP: a hierarchical method and a dynamic method. We further perform some ablation studies on the performance of the models by investigating different hyperparameters and various architecture changes.