用于模拟赛车的不断发展的控制器
Evolving controllers for simulated car racing
摘要：
本文介绍了一个模拟无线电遥控赛车在一个轨道上，在一个真正的物理轨道建模的控制器的演变。比较了基于神经网络、力场和动作序列的五种不同控制器结构。控制器使用自我中心（第一人称），牛顿（第三人称）或没有关于汽车状态的信息（开环控制器）。唯一能够进化出良好赛车行为的控制器是基于一个作用于自我中心输入的神经网络。
英文摘要：
This paper describes the evolution of controllers for racing a simulated radio-controlled car around a track, modelled on a real physical track. Five different controller architectures were compared, based on neural networks, force fields and action sequences. The controllers use either egocentric (first person), Newtonian (third person) or no information about the state of the car (open-loop controller). The only controller that was able to evolve good racing behaviour was based on a neural network acting on egocentric inputs.
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。
