基于深度Q值网络的自动小车控制方法

随着计算机技术和人工智能的飞速发展，无人驾驶车辆成为了一个新的热点。提出了一种自动小车的验证模型来模拟无人车，并验证了深度Q值网络（deep Q network, DQN）算法对自动小车的控制。该算法使用了强化学习和神经网络技术，能够在缺乏先验知识的情况下，根据获取的传感器信息训练神经网络，然后做出正确的决策，实现对车辆的控制，达到躲避障碍物的效果。此外，通过在模拟环境下的实验验证了DQN算法对自动小车的控制效果。实验结果表明，经过一定时间的训练，DQN算法可以有效的控制自动小车。

Control method of autonomous mini-car based on deep Q-network

With the rapid development of computer technology and artificial intelligence, unmanned vehicles have become a new hot spot. In this paper, a verification model of the automatic car is proposed to simulate the unmanned vehicle, and the deep Q network (DQN) algorithm is used to control the automatic car. The algorithm uses reinforcement learning and neural network technology, in the case of less prior knowledge, it can train the neural network according to the obtained sensor information ,then make the right decision to achieve the control of the vehicle and the effect of avoiding obstacles. In addition, this paper verifies the control effect of DQN algorithm on automatic trolley by experimenting in simulated environment. Experimental results show that, after a certain period of training, DQN algorithm can effectively control the automatic car.