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Controlling an Autonomous Vehicle with Deep Reinforcement Learning
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 per...
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Main Authors: | , , |
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Format: | Conference Proceeding |
Language: | English |
Citations: | Items that cite this one |
Online Access: | Request full text |
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Summary: | 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. |
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ISSN: | 2642-7214 |
DOI: | 10.1109/IVS.2019.8814124 |