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Found 3 papers, 1 papers with code
Learning to Evolve on Dynamic Graphs
In this paper, we propose Learning to Evolve on Dynamic Graphs (LEDG) - a novel algorithm that jointly learns graph information and time information.
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection
This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014).
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks
Domain adaptation using graph-structured networks learns label-discriminative and network-invariant node embeddings by sharing graph parameters.
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