# Deep Reinforcement Learning framework for Autonomous Driving

**Authors:** Ahmad El Sallab, Mohammed Abdou, Etienne Perot, Senthil Yogamani  
**arXiv:** [1704.02532](https://arxiv.org/abs/1704.02532)  
**Date:** 2017-04-08  
**Summary page:** https://www.summarizepaper.com/en/paper/1704.02532v1/

## Key points

- The paper explores the application of reinforcement learning in autonomous driving
- Authors propose a framework leveraging deep reinforcement learning techniques for interacting with dynamic environments effectively
- Reinforcement learning allows machines to learn through interaction and from mistakes
- Framework inspired by Google DeepMind's success in teaching machines to play games like Atari and Go
- Approach shows promise in handling partially observable scenarios through information integration
- Attention models integrated to focus on relevant information and reduce computational complexity
- Framework tested successfully in TORCS simulator for autonomous maneuvering in complex scenarios
- Lane-keeping assist algorithms used as a sample application to further test and refine the framework
- Research contributes to advancing autonomous driving by addressing challenges effectively

## Summary

The paper titled "Deep Reinforcement Learning framework for Autonomous Driving" by Ahmad El Sallab, Mohammed Abdou, Etienne Perot, and Senthil Yogamani explores the application of reinforcement learning in the field of autonomous driving. The authors propose a framework that leverages deep reinforcement learning techniques to address challenges related to interacting with dynamic environments effectively. This is particularly relevant as autonomous driving poses complex interactions with the environment such as other vehicles, pedestrians, and roadworks. <br>
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Reinforcement learning is a powerful  that allows machines to learn through interaction with their environment and learn from their mistakes. Despite its potential utility, it has not yet been successfully implemented in automotive applications. Motivated by the success of Google DeepMind in teaching machines to play Atari games and Go, the authors propose a framework for autonomous driving using . This approach has shown promise in handling partially observable scenarios through the incorporation of  for information integration. Additionally, attention models are integrated to focus on relevant information and reduce computational complexity for deployment on embedded hardware.<br>
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The proposed framework was tested in an open-source 3D car racing simulator called TORCS. In their simulation results, the authors demonstrate successful learning of autonomous maneuvering in scenarios involving complex road curvatures and simple interactions with other vehicles. The paper also discusses the use of lane-keeping assist algorithms as a sample application to further test and refine the proposed framework.<br>
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In conclusion,<br>
this research contributes to advancing the field of autonomous driving by leveraging deep reinforcement learning techniques to address challenges related to interacting with dynamic environments effectively.