David Abel

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20212026

λ

) for Modern Reinforcement Learning

1 citations · 1 across the 2 of their papers we have counts for

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cs.LG2025

Optimizing Return Distributions with Distributional Dynamic Programming

Bernardo Ávila Pires, Mark Rowland, Diana Borsa +6

We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standard reinforcement learning as a special ca…

cs.LG2021★ 1 cited

Revisiting Peng's Q( $λ$ ) 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…

Optimizing Return Distributions with Distributional Dynamic Programming

Revisiting Peng's Q(λ) for Modern Reinforcement Learning

Revisiting Peng's Q( $λ$ ) for Modern Reinforcement Learning