Simulated Autonomous Driving on Realistic Road Networks using Deep
  Reinforcement Learning
  Using Deep Reinforcement Learning (DRL) can be a promising approach to handle
various tasks in the field of (simulated) autonomous driving. However, recent
publications mainly consider learning in unusual driving environments. This
paper presents Driving School for Autonomous Agents (DSA^2), a software for
validating DRL algorithms in more usual driving environments based on
artificial and realistic road networks. We also present the results of applying
DSA^2 for handling the task of driving on a straight road while regulating the
velocity of one vehicle according to different speed limits.
