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2 results for “constrained RL”
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.
Aug 24, 2026
Robust Peak-cost Constrained Reinforcement Learning
Researchers introduced Robust Peak-cost Constrained Reinforcement Learning (RP-CRL), a new RL framework designed to bound the maximum cost incurred along any single trajectory—addressing safety-critical failure modes that standard cumulative-cost methods overlook.
Jul 20, 2026