自驾驶智能体拓扑扩充的Q-学习和NeuroEvolution比较研究
Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents
摘要：
自各种任务的自动化开始以来，自动驾驶车辆一直受到人们的浓厚兴趣。人类容易疲劳，在道路上的响应时间缓慢，除此之外，驾驶已经是一项相当危险的任务，每年约有135万起道路交通事故死亡。预计自动驾驶可以减少世界各地的驾驶事故数量，这就是为什么这个问题一直受到研究人员的浓厚兴趣。目前，自动驾驶车辆在使车辆自动化的过程中对各种子问题使用不同的算法。我们将重点介绍强化学习算法，更具体地说，Q-学习算法和增强拓扑的神经进化（NEAT），进化算法和人工神经网络的组合，训练模型代理学习如何在给定的路径上驾驶。
英文摘要：
Autonomous driving vehicles have been of keen interest ever since automation of various tasks started. Humans are prone to exhaustion and have a slow response time on the road, and on top of that driving is already quite a dangerous task with around 1.35 million road traffic incident deaths each year. It is expected that autonomous driving can reduce the number of driving accidents around the world which is why this problem has been of keen interest for researchers. Currently, self-driving vehicles use different algorithms for various sub-problems in making the vehicle autonomous. We will focus reinforcement learning algorithms, more specifically Q-learning algorithms and NeuroEvolution of Augment Topologies (NEAT), a combination of evolutionary algorithms and artificial neural networks, to train a model agent to learn how to drive on a given path. This paper will focus on drawing a comparison between the two aforementioned algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
