Data-driven regularized least squares with self-normalized bounds and lattice abstraction yields certified (N, ε)-PCIS for linear MDPs via conservative backward recursion.
A near-optimal algorithm for safe reinforcement learning under instantaneous hard constraints
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Data-Driven Synthesis of Probabilistic Controlled Invariant Sets for Linear MDPs
Data-driven regularized least squares with self-normalized bounds and lattice abstraction yields certified (N, ε)-PCIS for linear MDPs via conservative backward recursion.