부산대학교 도서관

Reinforcement Learning (RL) is an area of Machine Learning (ML) that takes care of what decisions are better to make with no prior information; it creates its own datasets via reward shaping. It has taken importance in the last years in the industrial field since it is a significant tool to make decisions under uncertainty. However, there is a lack of research in RL projects that contribute to develop innovative education in the professional education context. Therefore, in this work, a possible contribution of the AWS DeepRacer models to the autonomous driving for handicapped people is briefly presented in a theoretical way. Besides, through RL, three time-trial models were developed using Amazon Web Services (AWS) (AWS DeepRacer and AWS Console). These three models were developed for the first stage of the study, where there are no obstacles for the car. The resultant performance of each model is presented and discussed. Lastly, for future work three extra stages of the work are proposed: field tests of the presented models, static obstacles reward function design, and dynamic obstacles reward function development.