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Sean Meyn

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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?