DeepAI AI Chat
Log In Sign Up

Exploring applications of deep reinforcement learning for real-world autonomous driving systems

01/06/2019
by   Victor Talpaert, et al.
0

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthetic car simulator environments such as TORCS and CARLA. In general, DRL is still at its infancy in terms of usability in real-world applications. Our goal in this paper is to encourage real-world deployment of DRL in various autonomous driving (AD) applications. We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD systems. We then discuss the challenges which must be addressed to enable further progress towards real-world deployment.

READ FULL TEXT
02/13/2023

Review of Deep Reinforcement Learning for Autonomous Driving

Since deep neural networks' resurgence, reinforcement learning has gradu...
04/24/2019

How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning

Machine learning has been widely applied to various applications, some o...
10/16/2022

The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning

Evaluations of Deep Reinforcement Learning (DRL) methods are an integral...
01/06/2021

A Survey of Deep RL and IL for Autonomous Driving Policy Learning

Autonomous driving (AD) agents generate driving policies based on online...
11/22/2022

Don't Watch Me: A Spatio-Temporal Trojan Attack on Deep-Reinforcement-Learning-Augment Autonomous Driving

Deep reinforcement learning (DRL) is one of the most popular algorithms ...
06/16/2022

GMI-DRL: Empowering Multi-GPU Deep Reinforcement Learning with GPU Spatial Multiplexing

With the increasing popularity of robotics in industrial control and aut...