New Adaptive Dynamic Programming Structures From Neurocognitive Psychology and Graphical Games

该项目汇集了一位顶级控制工程师和一位认知神经科学家，目的是设计一系列新的控制设计，这些设计将复制和解释活体大脑的关键功能，这些功能在工程（或计算神经科学中使用的模型）中从未实现过。这项新工作建立在Frank Lewis之前的工作基础上，他开发了自适应控制器（RLADP），在面对非线性和需要远见的挑战（如电网管理）时，可以随着时间的推移最大化性能。在这里，该小组将试图解释和复制大脑如何处理挑战，这些挑战需要他们学习如何组织时间，在多个时间范围内进行多层次的决策，以及如何处理空间中的复杂结构，就像我们需要管理复杂的基础设施网络一样。这两个挑战基本上解决了当今最好的RLADP与哺乳动物大脑最高能力之间的三个差距中的两个。新的基础设计工作将用于正在进行的电力微电网控制的实验室工作，微电网是未来电力分配网络的重要组成部分，能够应对插电式混合动力汽车或屋顶太阳能等的大规模渗透。之前的工作在Lewis和Liu编辑的IEEE/Wiley RLADP手册中进行了回顾。空间复杂性将通过考虑在图上定义的系统的最优控制来解决，例如电力和通信网络。博弈论的扩展，有关问题的分布式或集体智能，也将考虑。这项工作还将建立在共同负责人莱文（co-PI Levine）对大脑机制模型的研究基础上，该模型涉及杏仁核中的情绪门和前扣带皮层中的慎重决定。

This project brings together a top control engineer with a cognitive neuroscientist, in order to design a new family of control designs which would replicate and explain key capabilities of living brains which have never yet been achieved in engineering (or in the models used in computational neuroscience). This new work builds on previous work by Frank Lewis, in developing adaptive controllers (RLADP) which can maximize performance over time, in the face of nonlinearity and challenges which require foresight, such as the management of power grids. Here, the group will try to explain and replicate how brains can also handle challenges which require them to learn how to structure time, with multiple levels of decision with multiple time horizons, and how to handle complex structure in space, as we need to in managing complex infrastructure networks. These two challenges essentially address two of the three gaps between today's best RLADP and the highest capabilities of the mammal brain. The new fundamental design work will feed into ongoing laboratory work in the control of electric power microgrids, which are important as building blocks for future electric power distribution networks capable of coping with large penetrations of plug-in hybrid cars or rooftop solar and the like. The previous work is reviewed in the Handbook of RLADP, from IEEE/Wiley, edited by Lewis and Liu. Spatial complexity will be addressed by considering optimal control of systems defined over graphs, such as power and communication networks. Game theoretic extensions, related to issues of distributed or collective intelligence, will also be considered. The work will also build on work by co-PI Levine on models of mechanisms in the brain involving the emotional gates in the amygdala and deliberative decisions in the anterior cingulate cortex.