Design and Realization of Autonomous Cars Using Deep Q Learning

Danish Khan (Jamia Hamdard, New Delhi, India); Sameena Naaz (Jamia Hamdard, New Delhi, United Kingdom (Great Britain)); Farheen Siddiqui (Jamia Hamdard, India)

Self driving cars are one of the most acclaimed technologies of the 21st century after the internet, but they have become a bone of contention among orthodox drivers. With the evolution of advances in software such as reinforcement learning algorithms and Q-learning, the world of artificial intelligence has taken a big leap forward. These algorithms are nature inspired to categorize actions through a system of reward points and negative points. Our research reported here focuses on implementation techniques of such reinforcement algorithms in scenarios such as the self-driving car. In this work we refer to the Bellman equation to give rewards for certain actions and the Markov decision processes for decision-making which includes a certain degree of randomness in the self-driving car and make compromises to reach its destination.

Journal: International Journal of Simulation- Systems, Science and Technology- IJSSST V20

Published: Feb 28, 2019

DOI: 10.5013/IJSSST.a.20.01.26