Simulated Autonomous Driving in a Realistic Driving Environment using
  Deep Reinforcement Learning and a Deterministic Finite State Machine
  In the field of Autonomous Driving, the system controlling the vehicle can be
seen as an agent acting in a complex environment and thus naturally fits into
the modern framework of Reinforcement Learning. However, learning to drive can
be a challenging task and current results are often restricted to simplified
driving environments. To advance the field, we present a method to adaptively
restrict the action space of the agent according to its current driving
situation and show that it can be used to swiftly learn to drive in a realistic
environment based on the Deep Q-Network algorithm.
