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

Understand

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.