论文标题
对自动驾驶剂增强拓扑的Q学习和神经进化的比较研究
Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents
论文作者
论文摘要
自从各种任务的自动化开始以来，自动驾驶车辆一直引起人们的兴趣。人类容易疲惫，在道路上的反应时间缓慢，最重要的是，每年约有135万道路交通事故死亡，这已经是一项危险的任务。可以预期，自主驾驶可以减少世界上驾驶事故的数量，这就是为什么这个问题对研究人员感兴趣的原因。当前，自动驾驶汽车在使车辆自动驾驶时使用不同的算法来实现各种子问题。我们将重点关注增强学习算法，更具体地说是Q学习算法和增强拓扑的神经进化（NEAT），即进化算法和人工神经网络的结合，以训练模型代理，以学习如何在给定路径上驱动。本文将重点介绍上述两种算法之间的比较。
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.
