The invention provides an urban ecological road space driving behavior optimization method based on reinforcement learning. The method comprises the following steps: step 1, obtaining a simulation driving environment interacting with a Q-Learning algorithm; 2, designing a state space based on a simulation driving environment; step 3, designing an action space based on the simulated driving environment; 4, designing a reward function based on the state space; and step 5, training the Q-Learning algorithm until convergence, and updating the Q-Learning algorithm by using a greedy strategy in the training process. The method has good fitness to an actual scene, the solving efficiency of the network connection vehicle operation model is improved, the greedy decision algorithm is introduced to be combined with the reinforcement learning algorithm, the randomness of the algorithm in iterative learning is weakened, an exploration mechanism is established, the learning iteration speed of the algorithm is increased, and the network connection vehicle operation model solving efficiency is improved. And the limitation of solving the ecological road vehicle operation model caused by the animal passing problem in the prior art is made up.
本发明提出了一种基于强化学习的城市生态道路空间驾驶行为优化方法,包括以下步骤:步骤1、获取与Q‑Learning算法进行交互的仿真驾驶环境;步骤2、基于仿真驾驶环境,设计状态空间;步骤3、基于仿真驾驶环境,设计动作空间;步骤4、基于状态空间,设计奖励函数;步骤5、训练Q‑Learning算法至收敛,在训练过程中Q‑Learning算法使用贪婪策略进行更新。本发明对实际场景有较好的贴合性,提升了网联车辆运行模型的求解效率,并引入贪婪决策算法结合强化学习算法,减弱算法在迭代学习中的随机性,建立探索机制,加快算法的学习迭代速度,弥补了现有的由于动物穿行问题造成的生态道路车辆运行模型求解的局限。
Urban ecological road space driving behavior optimization method based on reinforcement learning
基于强化学习的城市生态道路空间驾驶行为优化方法
2024-11-15
Patent
Electronic Resource
Chinese
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