most citedLearning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning

331 citations · 357 across the 4 of their papers we have counts for

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cs.LG2021★ 3 cited

Continuous-Depth Neural Models for Dynamic Graph Prediction

Michael Poli, Stefano Massaroli, Clayton M. Rabideau +4

We introduce the framework of continuous-depth graph neural networks (GNNs). Neural graph differential equations (Neural GDEs) are formalized as the counterpart to GNNs where the i…

cs.LG2021★ 22 cited

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park

We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic rew…