  We propose an inverse reinforcement learning (IRL) approach using Deep
Q-Networks to extract the rewards in problems with large state spaces. We
evaluate the performance of this approach in a simulation-based autonomous
driving scenario. Our results resemble the intuitive relation between the
reward function and readings of distance sensors mounted at different poses on
the car. We also show that, after a few learning rounds, our simulated agent
generates collision-free motions and performs human-like lane change behaviour.
