Manuscript Number : CSEIT19482
Survey on Autonomous Vehicle Control Using Reinforcement Learning
Authors(6) :-Mukesh Iyer, Nilesh Nanda, Jai Baheti, Rajmohan, Bajaj, Dr. Sunil Rathod In this paper we study the demonstration of the application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, the model is able to learn a policy for lane following in a handful of training episodes using a single monocular image as input. The paper provides a general and easy to obtain reward: the distance travelled by the vehicle without going of the lane. Model use a continuous, model free deep reinforcement learning algorithm, with all exploration and optimisation performed on-vehicle.This demonstrates a new framework for autonomous driving which moves away from reliance on pre determined logical rules, mapping, and direct supervision. The paper discusses the challenges and opportunities to scale this approach to a broader range of autonomous driving tasks.
Mukesh Iyer Reinforcement Learning, Deep Learning, Self-Driving Cars, Imitation Learning, Autonomous cars and Lane Detection. Publication Details Published in : Volume 4 | Issue 8 | September-October 2019 Article Preview
Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan Savitribai Phule Pune University, Pune, Maharashtra, India
Nilesh Nanda
Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan Savitribai Phule Pune University, Pune, Maharashtra, India
Jai Baheti
Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan Savitribai Phule Pune University, Pune, Maharashtra, India
Rajmohan
Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan Savitribai Phule Pune University, Pune, Maharashtra, India
Bajaj
Professor, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegoan Savitribai Phule Pune University, Pune, Maharashtra, India
Dr. Sunil Rathod
Date of Publication : 2019-10-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 05-09
Manuscript Number : CSEIT19482
Publisher : Technoscience Academy