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Sean Meyn

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