Self-Navigation Car using Reinforcement Learning
In this paper, a project is described which is a 2-D modelled version of a car that will learn how to drive itself. It will have to figure everything out on its own. In addition, to achieve that the simulator contains a car running simultaneously &can be controlled by different control algorithms - heuristic, reinforcement learning-based, etc. For each dynamic input, the Reinforcement- Learning modifies new patterns. Ultimately, Reinforcement Learning helps in maximizing the reward from every state. In this first Part, we will implement a Reinforcement-Learning model to build an AI for Self Driving Car. Project will be focusing on the brain of the car not any graphics. The car will detect obstacles and take basic actions. To make autonomous car or self-driving car a reality, some of the factors to be considered are human safety and quality of life.
 W. H. Organization. (2015). Global status report on road safety. Available at: https://www.who.int/violence_injury_prevention/road_safety_status/2015/en/.
 Daily M., Swarup, M., Trivedi, M. (2017). Self-driving cars. Computer, 50(12), 18-23. Available at: http://ieeexplore.ieee.org/document/8220479/.
 Gargi Sharma. (2017). How artificial intelligence is outpacing humans. Available at: https://dzone.com/articles/how-artificial-intelligence-is-outpacing-humans.
 D.J White. (1993). A survey of applications of markov decision processes. Available at: http://www.it.uu.se/edu/course/homepage/aism/st11/MDPApplications3.pdf.
Copyright (c) 2019 International Journal of Engineering and Management Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.