Machine Learning

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

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  • arXiv
    5 pts

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

    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.

    Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks
    by Sahand Sharifzadeh, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers
    https://arxiv.org/pdf/1612.03653v2.pdf

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