Autonomous RL: Autonomous Vehicle Obstacle Avoidance in a Dynamic Environment using MLP-SARSA Reinforcement Learning

Autonomous RL: Autonomous Vehicle Obstacle Avoidance in a Dynamic Environment using MLP-SARSA Reinforcement Learning
Title:
Autonomous RL: Autonomous Vehicle Obstacle Avoidance in a Dynamic Environment using MLP-SARSA Reinforcement Learning
Other Titles:
2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR)
Publication Date:
03 May 2019
Citation:
C. S. Arvind and J. Senthilnath, "Autonomous RL: Autonomous Vehicle Obstacle Avoidance in a Dynamic Environment using MLP-SARSA Reinforcement Learning," 2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR), Singapore, 2019, pp. 120-124. doi: 10.1109/ICMSR.2019.8835462
Abstract:
This paper presents a Multi-Layer Perceptron-State Action Reward State Action (MLP-SARSA) based reinforcement learning methodology for dynamic obstacle detection and avoidance for autonomous vehicle navigation. MLP-SARSA is an on-policy reinforcement learning approach, which gains information and rewards from the environment and helps the autonomous vehicle to avoid dynamic moving obstacles. MLP with SARSA provides a significant advantage over dynamic environment compared to other traditional reinforcement algorithms. In this study, a MLP-SARSA model is trained in a complex urban simulation environment with dynamic obstacles using the pygame library. Experimental results show that the trained MLP-SARSA can navigate the autonomous vehicle in a dynamic environment with more confidences than traditional Q-learning and SARSA reinforcement algorithms.
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Description:
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
978-1-7281-2223-6
978-1-7281-2222-9
978-1-7281-2224-3
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