# Deep Reinforcement Learning framework for Autonomous Driving

## TL;DR

This paper proposes a deep reinforcement learning framework for autonomous driving that uses RNNs and attention models to handle complex, partially observable environments, validated through simulation in TORCS.

## Contribution

It introduces a novel deep RL framework incorporating RNNs and attention mechanisms specifically designed for autonomous driving tasks.

## Key findings

- Successfully learned autonomous maneuvers in complex road scenarios
- Demonstrated the framework's ability to handle partial observability
- Reduced computational complexity with attention models

## Abstract

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning. This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks. As it is a relatively new area of research for autonomous driving, we provide a short overview of deep reinforcement learning and then describe our proposed framework. It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios. It also integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware. The framework was tested in an open source 3D car racing simulator called TORCS. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction of other vehicles.