Self-Driving Car Simulation Using Reinforcement Learning and Xception Model Tuning

The main idea is to have the environment (server) and then agents (clients). The server/client architecture means that the user can of course run both the server and client locally on the same machines, but it could also run the environment (server) on one machine and multiple clients on multiple other machines. For training and predicting at the same time, reinforcement learning is used. To obtain a convincing result in the training process would mean promising improvement in the field of automation and automotives. This simulation will be very helpful in all manners when developed with more real-life objects and circumstances that get into the simulation asset. The improvement in self-driving cars comes along with the advancement in the field of deep learning and data handling. Due to the sheer amount of multidimensional data that a self-driving car could create, a more efficient data handling methods are required to compute better results. There is always a chance to add layers to this particular deep learning framework and make it more efficient as the data captured evolves. The data may evolve in the fields of addition in new sensors, new obstacle identification, addition of more real time features. Progressing further into this study and implementation of a deep learning framework for self-driving cars, it is observed that the sensor data provides sufficient parameters for the estimation of new class variables. The model accuracy and results are compared in a way that is projected towards higher volume of data along with high computational power. This study recommends using cloud service to accommodate the graphical and CPU requirements as the computation for this particular multidimensional data could be very demanding. The understanding of how autonomous vehicles and how well it would be accepted is discussed below, along with the complications it would bring. It's a social change which many will oppose in the beginning but will slowly adopt as science improves and moves forward.