Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks2017 Jan 21
Para quem quiser saber um pouco mais das evoluções em relação a aplicação de aprendizado por reforço e Deep Learning em sistemas autônomos, esse paper é uma boa pedida.
Abstract: We propose an inverse reinforcement learning (IRL) approach using Deep QNetworks 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.
Conclusions: In this paper we proposed using Deep Q-Networks as the refinement step in Inverse Reinforcement Learning approaches. This enabled us to extract the rewards in scenarios with large state spaces such as driving, given expert demonstrations. The aim of this work was to extend the general approach to IRL. Exploring more advanced methods like Maximum Entropy IRL and the support for nonlinear reward functions is currently under investigation.