231 citations · 394 across the 11 of their papers we have counts for
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cs.LG2021★ 1 cited
Revisiting Peng's Q() for Modern Reinforcement Learning
Tadashi Kozuno, Yunhao Tang, Mark Rowland +5
Off-policy multi-step reinforcement learning algorithms consist of conservative and non-conservative algorithms: the former actively cut traces, whereas the latter do not. Recently…
cs.LG2021★ 12 cited
Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning
Víctor Campos, Pablo Sprechmann, Steven Hansen +5
Designing agents that acquire knowledge autonomously and use it to solve new tasks efficiently is an important challenge in reinforcement learning. Knowledge acquired during an uns…