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Found 3 papers, 2 papers with code
Diverse Policy Optimization for Structured Action Space
We propose a simple and effective RL method, Diverse Policy Optimization (DPO), to model the policies in structured action space as the energy-based models (EBM) by following the probabilistic RL framework.
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features
In this case, GNNs can barely learn useful information, resulting in prohibitive difficulty in making actions for successively rewiring edges under a reinforcement learning context.
Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets
Differently, in this paper, we are interested in a novel problem named Time Series Conditioned Graph Generation: given an input multivariate time series, we aim to infer a target relation graph modeling the underlying interrelationships between time series with each node corresponding to each time series.
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