331 citations · 356 across the 3 of their papers we have counts for
3 papers
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…
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…
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning
Junyoung Park, Jaehyeong Chun, Sang Hun Kim +2
We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and reinforcement learning (RL). We formulate the scheduling process of JSS…