153 citations · 251 across the 11 of their papers we have counts for
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cs.LG2021★ 1 cited
Learning Expected Emphatic Traces for Deep RL
Ray Jiang, Shangtong Zhang, Veronica Chelu +2
Off-policy sampling and experience replay are key for improving sample efficiency and scaling model-free temporal difference learning methods. When combined with function approxima…
cs.LG2021★ 3 cited
Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhongwen Xu +4
Off-policy learning allows us to learn about possible policies of behavior from experience generated by a different behavior policy. Temporal difference (TD) learning algorithms ca…