424 citations · 1.3k across the 18 of their papers we have counts for
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cs.LG2023★ 2 cited
Deep Reinforcement Learning with Plasticity Injection
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski +4
A growing body of evidence suggests that neural networks employed in deep reinforcement learning (RL) gradually lose their plasticity, the ability to learn from new data; however,…
cs.LG2023★ 3 cited
An Analysis of Quantile Temporal-Difference Learning
Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar +6
We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key component in several successful large-scale ap…