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Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates
We provide a new understanding of the stochastic gradient bandit algorithm by showing that
it converges to a globally optimal policy almost surely using\emph {any} constant learning …
it converges to a globally optimal policy almost surely using\emph {any} constant learning …
Fast Convergence of Softmax Policy Mirror Ascent
Natural policy gradient (NPG) is a common policy optimization algorithm and can be viewed
as mirror ascent in the space of probabilities. Recently, Vaswani et al.[2021] introduced a …
as mirror ascent in the space of probabilities. Recently, Vaswani et al.[2021] introduced a …
Fast Convergence of Softmax Policy Mirror Ascent for Bandits & Tabular MDPs
We analyze the convergence of a novel policy gradient algorithm (referred to as SPMA) for
multi-armed bandits and tabular Markov decision processes (MDPs). SPMA is an …
multi-armed bandits and tabular Markov decision processes (MDPs). SPMA is an …