Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation

no code implementations • 29 Jul 2020 • Yunus Bicer, Ali Alizadeh, Nazim Kemal Ure, Ahmetcan Erdogan, Orkun Kizilirmak

The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driver\'s policy.

Imitation Learning Self-Driving Cars