  Current state of the art solutions in the control of an autonomous vehicle
mainly use supervised end-to-end learning, or decoupled perception, planning
and action pipelines. Another possible solution is deep reinforcement learning,
but such a method requires that the agent interacts with its surroundings in a
simulated environment. In this paper we introduce GridSim, which is an
autonomous driving simulator engine running a car-like robot architecture to
generate occupancy grids from simulated sensors. We use GridSim to study the
performance of two deep learning approaches, deep reinforcement learning and
driving behavioral learning through genetic algorithms. The deep network
encodes the desired behavior in a two elements fitness function describing a
maximum travel distance and a maximum forward speed, bounded to a specific
interval. The algorithms are evaluated on simulated highways, curved roads and
inner-city scenarios, all including different driving limitations.
