Paper characterizes constrained gradient equilibrium under local adversaries
Constrained gradient equilibrium can fail under simple constraints unless the adversary is locally restricted.
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Constrained gradient equilibrium can fail under simple constraints unless the adversary is locally restricted.
A rare-switching bandit algorithm keeps exact sliding-window feasibility with near-optimal regret.
Paper unifies regularization-based robust RL methods via new performance-gap upper bounds and jointly learned Lagrange multipliers.
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