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域，例如物流，应急响应，弹性电网和机器人技术，都需要打招呼，以在紧缩的时间表下做出高质量的决策。该项目满足了通过新理论和加速学习的高质量，但有效的决策的需求，这将使计划者能够根据先前的Plansexperience加速其绩效。这种加速学习的方法受到这样一个事实的启发，即人类通常会学会通过经验来加快推理程序，而不会牺牲决策质量。通过加速学习，一种效率低下的计划师可以将高质量的决策效率地转化为在质量上损失的损失，从而涉及质量的损失。首先，通过引入规范的加速学习（PSL）的规范问题并研究如何使用PSL来解决各种加速目标，从而形式化了加速学习问题。其次，一个新颖的在线规划框架涵盖了许多现有的框架，并且正在设计和开发许多潜在的加速机会。第三，该项目正在为新框架生成新的加速学习学位，这些框架学习了各种类型的知识，并且可以利用深层神经网络（DNN）技术。最后，这项研究正在产生广泛的经验评估，包括对电网控制，市政应急响应和基准标准域的重要问题的应用。该项目有可能在必须在整体环境中做出时间敏感的决策来实现更广泛的影响攻击。它将直接促进INTWO申请的进步：补救措施控制无电网电网以最大程度地减少级联停电，以及对诸如消防和救援行动的诸如诸如消防和救援行动的紧急情况的计划。该项目还将有助于通过研究助手来提高研究生教育，并通过夏季和学术术语研究经验。 Aspecial主题研究生课程将在俄勒冈州立大学的规划和学习领域进行教授，所有Coursemestrials将是开放的。