ALaM stabilizes state-wise multiplier networks in safe RL via quadratic penalties and supervised regression on dual targets, guaranteeing multiplier convergence and optimal constrained policies when combined with SAC.
Brezis,Functional Analysis, Sobolev Spaces and Partial Differential Equations
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Augmented Lagrangian Multiplier Network for State-wise Safety in Reinforcement Learning
ALaM stabilizes state-wise multiplier networks in safe RL via quadratic penalties and supervised regression on dual targets, guaranteeing multiplier convergence and optimal constrained policies when combined with SAC.