摘要：

传统的强化学习方法受离散状态空间和离散动作空间的限制,不能很好地应用于匝道汇入场景。为此,构建一种基于强化学习的无人驾驶匝道汇入模型。使用深度Q网络构建强化学习模型,依据该模型将匝道汇入问题纳入强化学习问题的范畴后进行求解。实验结果表明,该模 型可以针对不同的环境车辆速度采取不同的策略,从而提高无人驾驶在匝道汇入场景下的智能化决策水平。

关键词: 无人驾驶, 决策, 匝道汇入, 强化学习, 深度Q网络

Abstract:

The traditional reinforcement learning method is limited by discrete state space and discrete action space,and can not be applied to ramp merging scene.Therefore,a reinforcement learning based autonomous driving ramp merging model is constructed.The reinforcement learning model is built by deep Q network.The ramp merging problem is incorporated into the category of reinforcement learning problem and solved.Experimental results show that the model can adopt different strategies for different environment vehicle speeds,thus improving the intelligent decision-making level of the autonomous driving in ramp merging scene.

Key words: autonomous driving, decision-making, ramp merging, reinforcement learning, deep Q network

中图分类号: 

• TP393