Contextual-Bandit-Regret-Risk-Dataset

This dataset simulates cumulative regret growth for bandit algorithms under stationary and drifting reward conditions. The simulation spans 2800 configurations across five India-relevant tasks (vernacular content recommendation, dynamic pricing, agri-advisory messaging, adaptive clinical trials, ad placement), seven algorithms with published regret-bound guarantees, arm or context scale, and drift rate, with an explicit flag for whether a drift-aware mechanism is used. Ten grounding sources are documented in bandit_theory.py and the README Research basis, including UCB1, Thompson Sampling, LinUCB, and Besbes, Gur and Zeevi's non-stationary bandit theory.