# Object-sensitive Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1809.06064 · HTML: https://mlsanity.com/paper/1809.06064/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 10 of 10)

- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks](https://mlsanity.com/paper/2108.01176/) (2021)
- [Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero](https://mlsanity.com/paper/2310.16410/) (2023)
- [OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments](https://mlsanity.com/paper/2306.08649/) (2023)
- [AdaZoom: Adaptive Zoom Network for Multi-Scale Object Detection in Large Scenes](https://mlsanity.com/paper/2106.10409/) (2021)
- [Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning](https://mlsanity.com/paper/2504.02999/) (2025)
- [A Bayesian Approach to Identifying Representational Errors](https://mlsanity.com/paper/2103.15171/) (2021)
- [Toward Debugging Deep Reinforcement Learning Programs with RLExplorer](https://mlsanity.com/paper/2410.04322/) (2024)
- [Enhancing Learning Path Recommendation via Multi-task Learning](https://mlsanity.com/paper/2507.05295/) (2025)
- [DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detection](https://mlsanity.com/paper/2508.18474/) (2025)

## Abstract (arXiv)

Deep reinforcement learning has become popular over recent years, showing superiority on different visual-input tasks such as playing Atari games and robot navigation. Although objects are important image elements, few work considers enhancing deep reinforcement learning with object characteristics. In this paper, we propose a novel method that can incorporate object recognition processing to deep reinforcement learning models. This approach can be adapted to any existing deep reinforcement learning frameworks. State-of-the-art results are shown in experiments on Atari games. We also propose a new approach called "object saliency maps" to visually explain the actions made by deep reinforcement learning agents.