RI: Small: Speedup Learning for Online Planning Under Uncertainty

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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.