Graph-RHO is a critical-path-aware heterogeneous graph network for rolling horizon optimization in flexible job-shop scheduling that achieves state-of-the-art solution quality and over 30% faster solve times on large instances.
Model predictive control: Theory and practice—a survey,
4 Pith papers cite this work. Polarity classification is still indexing.
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A nested dynamic program using the Regret-Bellman operator computes regret-optimal policies that interpolate between MDP and robust controllers for finite-state systems.
Contextual PPC uses world-model score densities to impose Riemannian structure on actions, yielding a safety bound on manifold distance controlled by estimation error and barrier curvature that improves with richer context.
Meta-learning framework adapting iMAML for rapid controller tuning on uncertain nonlinear systems via offline source data and limited online target adaptation, shown with neural state-space and DQN variants.
citing papers explorer
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Graph-RHO: Critical-path-aware Heterogeneous Graph Network for Long-Horizon Flexible Job-Shop Scheduling
Graph-RHO is a critical-path-aware heterogeneous graph network for rolling horizon optimization in flexible job-shop scheduling that achieves state-of-the-art solution quality and over 30% faster solve times on large instances.
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Regret-Optimal Control for Finite-State Systems
A nested dynamic program using the Regret-Bellman operator computes regret-optimal policies that interpolate between MDP and robust controllers for finite-state systems.
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Safety-Critical Contextual Control via Online Riemannian Optimization with World Models
Contextual PPC uses world-model score densities to impose Riemannian structure on actions, yielding a safety bound on manifold distance controlled by estimation error and barrier curvature that improves with richer context.
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Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems
Meta-learning framework adapting iMAML for rapid controller tuning on uncertain nonlinear systems via offline source data and limited online target adaptation, shown with neural state-space and DQN variants.