Abstract
Training an autonomous agent in the real world is a cumbersome process. The hardware modules required are expensive and they need routine maintenance. The data collection process is time-consuming and it is difficult to collect data in different conditions and scenarios. Moreover, testing these agents in the real world requires many permissions and could be potentially hazardous. This paper introduces a virtual environment for training and testing of autonomous driving agents. The environment has features like customizable car parameters and sensors, different terrains, customizable data extraction parameters, and simulated pedestrian and vehicular traffic. The environment can connect to any learning agent via a communication interface. Therefore, the environment introduced in this paper expedites the training and testing process and the learned knowledge representations can be scaled to the real world.
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