ReLearner: A Reinforcement Learning-Based Self Driving Car Model Using Gym Environment

Abstract

AbstractIn the recent past, Artificial intelligence and its sister technology such as Machine Learning, Deep Learning, and Reinforcement learning have grown rapidly in several applications. The self-driving car is one of the applications, which is the need of the hour. In this paper, we describe the trends in autonomous vehicle technology for the self-driving car. There are many different approaches to mathematically formulate a design for the self-driving car such as deep Q-learning, Q-learning, and machine learning. However, in this paper, we propose a very basic and less compute-intensive simplistic self-driving car model called “ReLearner” using the Gym environment. To simulate the self-driving car model, we preferred to create a simple small environment OpenAi gym which is a deterministic environment. The OpenAi gym provides the virtual simulation environment and parameter tuning to train and test the model. We have focused on two methods to test our model. The basic approach is to compare the performance of the car when tested using Q-Learning and another using a random action agent, i.e., No reinforcement learning. We have derived a theoretical model and analyzed how to use Q-learning to train cars to drive. We have carried out a simulation and on evaluating the performance and found that Q-learning is a more optimal approach to solve the issue of a self-driving car.KeywordsSelf-driving carDeep Q-learningQ-LearningReinforcement learningMachine learningDeterministic environmentVirtual simulation