1.2k citations · 4.5k across the 31 of their papers we have counts for
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cs.LG2020★ 81 cited
Revisiting Fundamentals of Experience Replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal +4
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding. We therefore present a systematic…
cs.LG2020★ 3 cited
On Catastrophic Interference in Atari 2600 Games
William Fedus, Dibya Ghosh, John D. Martin +3
Model-free deep reinforcement learning is sample inefficient. One hypothesis -- speculated, but not confirmed -- is that catastrophic interference within an environment inhibits le…