An integral function of fully autonomous robots and humans is the ability to focus attention on a few relevant percepts to reach a certain goal while disregarding irrelevant percepts. Humans and animals rely on the interactions between the Pre-Frontal Cortex (PFC) and the Basal Ganglia (BG) to achieve this focus called Working Memory (WM). The Working Memory Toolkit (WMtk) was developed based on a computational neuroscience model of this phenomenon with Temporal Difference (TD) Learning for autonomous systems. Recent adaptations of the toolkit either utilize Abstract Task Representations (ATRs) to solve Non-Observable (NO) tasks or storage of past input features to solve Partially-Observable (PO) tasks, but not both. We propose a new model, PONOWMtk, which combines both approaches, ATRs and input storage, with a static or dynamic number of ATRs. The results of our experiments show that PONOWMtk performs effectively for tasks that exhibit PO, NO, or both properties.
翻译:完全自主的机器人和人类的一个固有功能是能够集中关注几个相关的概念,以便在无视不相关概念的情况下达到某一目标。人类和动物依靠Fental Cortex(PFC)和Basal Ganglia(BG)之间的相互作用来实现这一重点,即工作记忆。工作记忆工具包(WMTK)是根据与时间差异(TD)为自主系统学习的这一现象的计算神经科学模型开发的。最近对工具包的调整,要么利用摘要任务说明(ATR)解决不可观测(NO)的任务,要么储存过去输入特征,以解决部分可观测(PO)的任务,但并非两者兼有。我们提出了一个新的模型,即PONOWMtk,将方法、ATRs和输入存储结合起来,并配有静态或动态的ATR。我们的实验结果表明,PONOWMtk为显示 PO、NO或两种属性的任务有效地履行了任务。