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stat.ML2019★ 24 cited
Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland, Robert Dadashi, Saurabh Kumar +3
We present a unifying framework for designing and analysing distributional reinforcement learning (DRL) algorithms in terms of recursively estimating statistics of the return distr…
stat.ML2019★ 1 cited
Optimistic optimization of a Brownian
Jean-Bastien Grill, Michal Valko, Rémi Munos
We address the problem of optimizing a Brownian motion. We consider a (random) realization of a Brownian motion with input space in . Given , our goal is to return an…