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

Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks