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Delay-corrected Bellman operator + causal attribution for constrained RL contraction proof under unknown stochastic delay [R]

A researcher proposes CCPL, a new constrained reinforcement learning framework that corrects for stochastic delays in consequence attribution using a delay-corrected Bellman operator and an Interventional Consequence Net (ICN), with a formal contraction proof but requiring known structural causal models for ICN pretraining.