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Stochastic Approximation and Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms
Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. Among many algorithms in machine learning, reinforcement learning algorithms such as TD- and Q-learning are two of its most famous applications. This talk will ...
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