Controlling an Autonomous Vehicle with Deep Reinforcement Learning

Abstract

We present a control approach for autonomous vehicles based on deep reinforcement learning. A neural network agent is trained to map its estimated state to acceleration and steering commands given the objective of reaching a specific target state while considering detected obstacles. Learning is performed using state-of-the-art proximal policy optimization in combination with a simulated environment. Training from scratch takes five to nine hours. The resulting agent is evaluated within simulation and subsequently applied to control a full-size research vehicle. For this, the autonomous exploration of a parking lot is considered, including turning maneuvers and obstacle avoidance. Altogether, this work is among the first examples to successfully apply deep reinforcement learning to a real vehicle.

arXiv commentAward as Best Student Paper at IEEE Intelligent Vehicles Symposium (IV), 2019

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• A Survey of Deep RL and IL for Autonomous Driving Policy Learning
• Deep Reinforcement Learning and Transportation Research: A Comprehensive Review
• Facilitating Connected Autonomous Vehicle Operations Using Space-weighted Information Fusion and Deep Reinforcement Learning Based Control
• A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7
• Time-Dependent Hybrid-State A* and Optimal Control for Autonomous Vehicles in Arbitrary and Dynamic Environment