scholarly journals Autonomous Vehicle Navigation Behavior Generation by Reinforcement Learning

1998 ◽  
Vol 10 (5) ◽  
pp. 413-417
Author(s):  
Keitaro Naruse ◽  
◽  
Yukinori Kakazu ◽  
Ming C. Leu ◽  
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Vehicle Navigation ◽  
Computational Simulations ◽  
Action Sequences ◽  
Autonomous Vehicle Navigation ◽  
Reinforcement Learning Algorithm

This paper presents an efficient reinforcement learning algorithm for autonomous vehicle navigation. Efficiency is achieved by identifying the structure of a given problem, and it is represented as a set of behaviors - efficient action sequences for solving the problem. Computational simulations are conducted and the proposed mechanism demonstrate.

scholarly journals Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1468
Author(s):  
Razin Bin Issa ◽  
Modhumonty Das ◽  
Md. Saferi Rahman ◽  
Monika Barua ◽  
Md. Khalilur Rhaman ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Navigation ◽  
Autonomous Vehicle ◽  
Dynamic Environment ◽  
Vehicle Navigation ◽  
Autonomous Agent ◽  
Learning Approach ◽  
Q Learning ◽  
Land Vehicles ◽  
Autonomous Vehicle Navigation

Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster R-CNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.

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