PACIFIER is a graph RL framework that matches analytical solvers for minimizing polarization and outperforms baselines across multiple intervention regimes on real Twitter networks up to 155k nodes by training on small synthetic graphs.
Rusu, Joel Veness, Marc G
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Hypernetworks map a forcing parameter directly to policy weights in an RL framework, enabling unified stabilization of the Kuramoto-Sivashinsky equation across regimes with KAN architectures showing strongest extrapolation.
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PACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework
PACIFIER is a graph RL framework that matches analytical solvers for minimizing polarization and outperforms baselines across multiple intervention regimes on real Twitter networks up to 155k nodes by training on small synthetic graphs.
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Hyperfastrl: Hypernetwork-based reinforcement learning for unified control of parametric chaotic PDEs
Hypernetworks map a forcing parameter directly to policy weights in an RL framework, enabling unified stabilization of the Kuramoto-Sivashinsky equation across regimes with KAN architectures showing strongest extrapolation.