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RI: Small: Speedup Learning for Online Planning Under Uncertainty

RI：小：加速不确定性下在线规划的学习

基本信息

批准号：
1619433
负责人：
Alan Fern
金额：
$ 45万
依托单位：
Oregon State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619433&HistoricalAwards=false
关键词：
RI Small Speedup Learning Online

项目摘要

Many complex stochastic planning domains such as logistics,emergency response, resilient power grids, and robotics require theability to make high-quality decisions under tight timeconstraints. This project addresses the need for high-quality, butcomputationally efficient, decision making via new theory andalgorithms for speedup learning, which will enable planners tolearn to speedup their performance based on prior planningexperience. This speedup-learning approach is loosely inspired bythe fact that humans routinely learn to speedup their reasoningprocesses with experience, without sacrificing decision quality.Similarly, through speedup learning, an inefficient planner thatproduces high-quality decisions will be transformed into a muchfaster planner with little loss in decision quality.The project involves advancing speedup learning for online planningunder uncertainty on four fronts. First, the speedup-learningproblem is formalized by introducing the canonical problem ofPrimitive Speedup Learning (PSL) and studying how PSL can be usedto solve various speedup objectives. Second, a novel onlineplanning framework, which subsumes many existing frameworks andenables many potential speedup opportunities, is being designed anddeveloped. Third, the project is producing new speedup learningalgorithms for the new framework, which learn various types ofknowledge and that can exploit deep neural network (DNN)techniques. Finally, the research is producing extensive empiricalevaluations including applications to the important problems ofpower grid control, municipal emergency response, and benchmarkplanning domains. The project has the potential for significant broader impact onapplications where time-sensitive decisions must be made withinstochastic environments. It will directly contribute to advances intwo applications in particular: remedial action control inelectrical grids to minimize cascading power outages, and planningfor municipal emergencies such as fire and rescue operations incities. The project will also serve to advance graduate educationthrough research assistantships and undergraduate education throughsummer and academic term research experiences for undergraduates. Aspecial topics graduate course will be taught on the area ofplanning and learning at Oregon State University and all coursematerials will be open access.

许多复杂的随机规划领域，如物流，应急响应，弹性电网和机器人技术，需要在严格的时间限制下做出高质量的决策。这个项目通过加速学习的新理论和算法来解决高质量但计算效率高的决策需求，这将使规划者能够根据先前的规划经验来学习加速他们的性能。这种加速学习方法的灵感大致来自于这样一个事实，即人类通常会根据经验加快推理过程，而不会牺牲决策质量。同样，通过加速学习，一个产生高质量决策的低效规划者将转变为一个速度更快的规划者，而决策质量几乎没有损失。该项目涉及在四个方面的不确定性下推进在线规划的加速学习。首先，通过引入原始加速学习（PSL）的规范问题，并研究如何使用PSL来解决各种加速目标，将加速学习问题形式化。第二，一个新的在线规划框架，它包含了许多现有的框架和denables许多潜在的加速机会，正在设计和开发。第三，该项目正在为新框架产生新的加速学习算法，该算法可以学习各种类型的知识，并可以利用深度神经网络（DNN）技术。最后，该研究正在产生广泛的实用性评估，包括应用于电网控制，市政应急响应和基准规划领域的重要问题。该项目有可能对那些必须在随机环境中做出时间敏感决策的应用程序产生更广泛的影响。它将直接促进两个应用领域的进步：电网中的补救行动控制，以最大限度地减少连锁停电，以及规划市政紧急情况，如火灾和救援行动。该项目还将通过研究助理奖学金和本科生暑期和学期研究经验来促进研究生教育。俄勒冈州州立大学将开设一门关于规划和学习领域的专题研究生课程，所有课程材料都将开放获取。