Offline-RL-Distribution-Shift-Risk-Model

This dataset simulates the central failure mode of offline reinforcement learning: a Q-function queried on out-of-distribution actions during the Bellman backup compounds unchecked error across bootstrapped updates until the policy diverges. The simulation spans 2475 configurations across five task archetypes, five D4RL-style dataset quality categories, eleven methods with different OOD-correction mechanisms, and three dataset sizes. Twelve grounding sources are documented in offline_rl_theory.py and the README Research basis, including BCQ, CQL, IQL, EDAC, MOPO, and COMBO.