Quasi-Stochastic Approximation: Algorithm Design Principles with Applications to Machine Learning and Optimization. DeepLearn2022 1. Goals & AlgorithmDesign.pdf DeepLearn2022 3. TD and Q Learning DeepLearn2022 2. Variance Matters Smart Grid Tutorial - January 2019 State Space Collapse in Resource Allocation for Demand Dispatch - May 2019 Irrational Agents and the Power Grid Zap Q-Learning - ISMP 2018 Introducing Zap Q-Learning Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms State estimation and Mean-Field Control with application to demand dispatch Demand-Side Flexibility for Reliable Ancillary Services Spectral Decomposition of Demand-Side Flexibility for Reliable Ancillary Services Demand-Side Flexibility for Reliable Ancillary Services in a Smart Grid: Eliminating Risk to Consumers and the Grid Why Do We Ignore Risk in Power Economics?