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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Learning Dynamic Priority Scheduling Heuristics with Graph Attention Networks
Committee:
Dr. Gombolay, Advisor
Dr. Klein, Co-Advisor
Dr. Chernova, Chair
Dr. Egerstedt
Abstract: The object of the proposed research is to develop a novel graph attention network-based framework to automatically learn scalable scheduling policies for resource optimization. We aim to tackle problems in the challenging stochastic environments, with two scenarios being considered. First, we consider scheduling with stochastic and dynamic task completion times in human-robot team coordination—we extend the multi-robot task scheduling problem by introducing human co-workers. Heterogeneous task completion time is considered for human and robot workers represented by different probabilistic distributions. Second, we consider scheduling with stochastic and dynamic task arrival and completion times in failure-predictive plane maintenance—duration of the plane repair or maintenance task is modeled by a probability distribution affected by the plane status. Furthermore, the policy needs to schedule under the uncertainty of plane failure before a repair task is issued that greatly influences the future repair cost. We propose to use imitation learning to learn from imperfect demonstrations and further improve the model performance through policy-based reinforcement learning. By parameterizing the learner with graph attention networks, our framework is computationally efficient and results in a scalable resource optimization scheduler.
