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Titel:

Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Patrick Hart, Alois Knoll
Abstract:
Learning-based approaches, such as reinforcement and imitation learning are gaining popularity in decision-making for autonomous driving. However, learned policies often fail to generalize and cannot handle novel situations well. Asking and answering questions in the form of "Would a policy perform well if the other agents had behaved differently?" can shed light on whether a policy has seen similar situations during training and generalizes well. In this work, a counterfactual policy evaluation...     »
Kongress- / Buchtitel:
IROS 2020 Workshop Perception, Learning, and Control for Autonomous Agile Vehicles
Jahr:
2020
WWW:
https://arxiv.org/abs/2003.11919
 BibTeX