Deakin University
Browse

File(s) under permanent embargo

Optimal autonomous driving through deep imitation learning and neuroevolution

conference contribution
posted on 2019-01-01, 00:00 authored by Seyed Mohammad Jafar Jalali, P M Kebria, Abbas KhosraviAbbas Khosravi, K Saleh, Darius Nahavandi, Saeid Nahavandi
© 2019 IEEE. Imitation learning is an efficient paradigm for teaching and controlling intelligent autonomous cars. Obtaining a set of suitable demonstrations to learn an end-to-end policy from raw pixels is a challenging task in imitation learning problems. Deep neural networks have recently shown outstanding results in learning from raw high dimensional data for solving a wide range of real-world applications. The success of deep neural networks depends on finding suitable hyperparameters for constructing network architecture. Besides, designing hand-crafted deep architectures is not an efficient way for achieving the best performance. To address this issue, this paper performs a neuro-evolution method based on genetic algorithm for finding the optimal deep neural networks architecture in terms of hyperparameters. The experimental results show the effectiveness of the proposed approach for training an autonomous vehicle.

History

Event

Systems, Man and Cybernetics. Conference (2019 : Bari, Italy)

Pagination

1215 - 1220

Publisher

IEEE

Location

Bari, Italy

Place of publication

Piscataway, N.J.

Start date

2019-10-06

End date

2019-10-09

ISSN

1062-922X

ISBN-13

9781728145693

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

SMC 2019 : Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics