The invention relates to an automatic driving automobile decision planning method based on maximum entropy reinforcement learning, and belongs to the field of automatic driving automobiles. The method comprises the following steps: S1, constructing a decision planning model based on maximum entropy reinforcement learning, wherein the model comprises a state space, an action space, a reward function, a strategy function and an evaluation model; s2, constructing an expressway simulation training scene with interactivity: describing movement of vehicles in the training scene by using a two-degree-of-freedom automobile kinematics model, and controlling environmental vehicles by using a rule-based intelligent decision planning model, so that the environment has interaction characteristics; and S3, training a decision planning model based on maximum entropy reinforcement learning. According to the method, the optimality and the stability of the expressway decision planning strategy of the automatic driving automobile are improved by utilizing maximum entropy reinforcement learning.
本发明涉及一种基于最大熵强化学习的自动驾驶汽车决策规划方法,属于自动驾驶汽车领域。该方法包括:S1:构建基于最大熵强化学习的决策规划模型,该模型包括:状态空间、动作空间、奖励函数、策略函数与评价模型;S2:构建具有交互性的高速公路仿真训练场景:使用二自由度汽车运动学模型描述训练场景中车辆的运动,并利用基于规则的智能决策规划模型控制环境车辆,使环境具有交互特性;S3:训练基于最大熵强化学习的决策规划模型。本发明利用最大熵强化学习提高了自动驾驶汽车的高速公路决策规划策略的最优性与稳定性。
Automatic driving automobile decision planning method based on maximum entropy reinforcement learning
基于最大熵强化学习的自动驾驶汽车决策规划方法
2022-06-03
Patent
Elektronische Ressource
Chinesisch
IPC: | G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / 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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