90 citations · 194 across the 7 of their papers we have counts for
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cs.LG2019
Momentum in Reinforcement Learning
Nino Vieillard, Bruno Scherrer, Olivier Pietquin +1
We adapt the optimization's concept of momentum to reinforcement learning. Seeing the state-action value functions as an analog to the gradients in optimization, we interpret momen…
cs.LG2019★ 90 cited
A Theory of Regularized Markov Decision Processes
Matthieu Geist, Bruno Scherrer, Olivier Pietquin
Many recent successful (deep) reinforcement learning algorithms make use of regularization, generally based on entropy or Kullback-Leibler divergence. We propose a general theory o…