Authors:
Anh T. Huynh
1
;
2
;
Ba-Tung Nguyen
1
;
2
;
Hoai-Thu Nguyen
1
;
2
;
Sang Vu
3
;
2
and
Hien D. Nguyen
4
;
2
Affiliations:
1
Faculty of Software Engineering, University of Information Technology, Ho Chi Minh City, Vietnam
;
2
Vietnam National University, Ho Chi Minh City, Vietnam
;
3
Faculty of Information Systems, University of Information Technology, Ho Chi Minh City, Vietnam
;
4
Faculty of Computer Science, University of Information Technology, Ho Chi Minh City, Vietnam
Keyword(s):
Autonomous Vehicles, Reinforcement Learning, Policy Gradient, Simulator, Software Engineering.
Abstract:
Nowadays autonomous driving is expected to revolutionize the transportation sector. Carmakers, researchers, and administrators have been working on this field for years and significant progress has been made. However, the doubts and challenges to overcome are still huge, regarding not only complex technologies but also human awareness, culture, current traffic infrastructure. In terms of technical perspective, the accurate detection of obstacles, avoiding adjacent obstacles, and automatic navigation through the environment are some of the difficult problems. In this paper, an approach for solving those problems is proposed by using of Policy Gradient to control a simulated car via reinforcement learning. The proposed method is worked effectively to train an agent to control the simulated car in Unity ML-agents Highway, which is a simulating environment. This environment is chosen from some criteria of an environment simulating autonomous vehicle. The testing of the proposed method go
t positive results. Beside the average speed was well, the agent successfully learned the turning operation, progressively gaining the ability to navigate larger sections of the simulated raceway without crashing.
(More)