This thrust focuses on decision making under uncertainty, both in centralized and decentralized settings, and for single and multi-agent environments. A key objective is to merge ideas from stochastic programming and reinforcement learning for solving multi-stage optimization problems under uncertainty. In this thrust, AI4OPT explores the forecasting of highly dimensional time series, uncertainty quantification, Bayesian optimization, and decentralization learning and optimization.