Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation

In this work, we present a data-driven simulation and training engine capable of learning end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging real, human-collected trajectories through an environment, we render novel training data that allows virtual agents to dri...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 5; H. 2; S. 1142 - 1149
Hauptverfasser:	Amini, Alexander, Gilitschenski, Igor, Phillips, Jacob, Moseyko, Julia, Banerjee, Rohan, Karaman, Sertac, Rus, Daniela
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.04.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Access control autonomous agents Autonomous vehicles data-driven simulation Deep learning in robotics and automation Machine learning Policies real world reinforcement learning Roads & highways Robust control Semantics Simulation Training
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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