Inverse Reinforcement Learning(IRL) is formulated within the framework of Markov decision process(MDP) where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform. Then the expert as trying to maximize a reward function that is expressible as a linear combination of known features specifying the reward function. However, in autonomous driving tasks, due to the difference of scene factor, such as obstacle and weather, the state spaces are frequently large and demonstrations can hardly visit all the states. it’s hard to get an optimal policy with RL method to express driver behavior model based on the reward which recovered with IRL method in this large-scale state space. In this paper, we focus on driving behavior modeling with IRL method which introduces the convolutional neural network to extract the associated state feature automatically, and express the policy by neural network to generalize the expert’s behaviors. Experimental results compared with the traditional end-to-end method on simulated vehicle show that the accuracy of decision-making greatly improved in the train curve, and in the new curve scene with a large number of unvisited state, this method shows a perfect generalization efficiency.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Inverse Reinforcement Learning via Neural Network in Driver Behavior Modeling


    Contributors:
    Zou, QiJie (author) / Li, Haoyu (author) / Zhang, Rubo (author)


    Publication date :

    2018-06-01


    Size :

    955552 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Modeling Driver Behavior using Adversarial Inverse Reinforcement Learning

    Sackmann, Moritz / Bey, Henrik / Hofmann, Ulrich et al. | IEEE | 2022



    Predicting Driver Behavior on the Highway with Multi-Agent Adversarial Inverse Reinforcement Learning

    Radtke, Henrik / Bey, Henrik / Sackmann, Moritz et al. | IEEE | 2023


    Anomalous ride-hailing driver detection with deep transfer inverse reinforcement learning

    Liu, Shan / Wang, Zhengli / Zhang, Ya et al. | Elsevier | 2023


    Car-Following Behavior Modeling With Maximum Entropy Deep Inverse Reinforcement Learning

    Nan, Jiangfeng / Deng, Weiwen / Zhang, Ruzheng et al. | IEEE | 2024