The invention discloses a traffic light control method based on deep reinforcement learning and inverse reinforcement learning, and the method comprises the steps: firstly building a Markov decision model of a traffic light control system, and building a traffic light control frame based on deep reinforcement learning according to an existing deep network model; the innovation point of the method is that a relative entropy inverse reinforcement learning algorithm is introduced to optimize the reward function design. According to the system state transition trajectory generated by expert decision, decision logic, namely a hidden reward function, contained in the expert is extracted through an inverse reinforcement learning algorithm, effective utilization of expert experience is achieved, and the algorithm has good robustness for noise in the expert trajectory. According to the method, the effect better than that of a traditional control scheme can be achieved in balanced traffic flow and non-balanced traffic flow scenes of a single intersection, and the control performance of a deep reinforcement learning algorithm is further improved.

    本发明公开了一种基于深度强化学习和逆强化学习的交通灯控制方法,首先建立交通灯控制系统的马尔科夫决策模型,并依据现有深度网络模型,搭建基于深度强化学习的交通灯控制框架。本发明的创新点在于引入了相对熵逆强化学习算法以优化奖励函数设计。根据专家决策生成的系统状态转移轨迹,通过逆强化学习算法提取专家内含的决策逻辑,即隐藏奖励函数,实现了对专家经验的有效利用,算法对专家轨迹中的噪声具有较好的鲁棒性。本发明能够在单个交叉路口的均衡车流和非均衡车流场景下,取得优于传统控制方案的效果,并进一步提升深度强化学习算法的控制性能。


    Access

    Download


    Export, share and cite



    Title :

    Traffic light control method based on deep reinforcement learning and inverse reinforcement learning


    Additional title:

    一种基于深度强化学习和逆强化学习的交通灯控制方法


    Contributors:
    ZHANG YA (author) / GU SHIYI (author) / CHEN GUOXI (author)

    Publication date :

    2023-03-07


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



    Deep reinforcement learning traffic light control method

    KONG YAN / LI YING / CHIH-CHAO YANG | European Patent Office | 2024

    Free access

    Deep Reinforcement Learning for Autonomous Traffic Light Control

    Garg, Deepeka / Chli, Maria / Vogiatzis, George | IEEE | 2018


    Traffic Light Control Using Reinforcement Learning

    Masfequier Rahman Swapno, S M / Nuruzzaman Nobel, SM / A C, Ramachandra et al. | IEEE | 2024


    Deep Reinforcement Learning-based Traffic Signal Control

    Ruan, Junyun / Tang, Jinzhuo / Gao, Ge et al. | IEEE | 2023


    Intelligent Traffic Light via Policy-based Deep Reinforcement Learning

    Zhu, Yue / Cai, Mingyu / Schwarz, Chris W. et al. | Springer Verlag | 2022