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Titel:

Graph-Based Autonomous Driving with Traffic-Rule-Enhanced Curriculum Learning

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Peiss, Lars Frederik; Wohlgemuth, Elias; Xue, Fan; Meyer, Eivind; Gressenbuch, Luis; Althoff, Matthias
Abstract:
Training reinforcement learning (RL) agents for motion planning in heavily constrained solution spaces may require extensive exploration, leading to long training times. In automated driving, RL agents have to learn multiple skills at once, such as collision avoidance, traffic rule adherence, and goal reaching. In this work, we decompose this complicated learning task by applying curriculum learning for the first time onto an RL agent based on graph neural networks. The curriculum's sequence of...     »
Kongress- / Buchtitel:
2023 IEEE International Conference on Intelligent Transportation Systems (ITSC)
Jahr:
2023
Copyright Informationen:
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