Controlling an Autonomous Vehicle with Deep Reinforcement Learning

2019-09-24 · Andreas Folkers, Matthias Rick, Christof Büskens

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.

📄 PDF Abstract BibTeX arXiv:1909.12153

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Autonomous VehiclesDeep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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