The invention discloses an automatic driving decision-making control method and system based on hierarchical reinforcement learning. The system comprises an action layer Agent and a decision-making layer Agent. The action layer Agent is a strategy-gradient-based reinforcement learning algorithm for processing continuous behaviors, and a correct action network is trained through interaction with road environment information; the decision-making layer Agent selects a value-based reinforcement learning algorithm for processing discrete behaviors, and a correct decision-making network is trained through interaction with traffic flow traffic state information. The action layer Agent is trained firstly, and the decision-making layer Agent is trained on the basis of the trained action layer Agent, so that two training tasks are clear, and the learning efficiency is improved. According to the invention, the situations of rapid acceleration and rapid deceleration are avoided, and the comfort ofthe whole vehicle is improved.
本发明公开了一种基于分层强化学习的自动驾驶决策控制方法及系统,包括动作层Agent和决策层Agent。动作层Agent为处理连续行为的基于策略梯度的强化学习算法,通过和道路环境信息交互训练出正确的动作网络;决策层Agent选择处理离散行为的基于价值的强化学习算法,通过和车流交通状态信息交互训练出正确的决策网络。通过先训练动作层Agent,在基于已经训练完成的动作层Agent的基础上训练决策层Agent,使两次训练任务明确,提高了学习效率。本发明避免了急加速和急减速的情况发生,改善了整车舒适性。
Automatic driving decision-making control method and system based on hierarchical reinforcement learning
一种基于分层强化学习的自动驾驶决策控制方法及系统
2020-10-30
Patent
Elektronische Ressource
Chinesisch
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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