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Beijing University of Technology
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Ditch the GNN training: this label propagation method matches or beats GNN accuracy while being far more computationally efficient, even on tricky heterophilous graphs.
Visualizing the critic's loss landscape reveals distinct characteristics linked to stable vs. unstable learning in online RL, offering a new window into algorithm dynamics.
Visualizing the loss landscape of off-policy RL critics reveals distinct geometric patterns linked to convergence and divergence, offering a new diagnostic tool for understanding optimization dynamics.
Uncover the hidden dynamics of your RL agent with a new visualization framework that reveals how TD errors sculpt the optimization landscape and drive policy updates.
