10 citations · 20 across the 3 of their papers we have counts for
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cs.LG2023★ 1 cited
Confronting Reward Model Overoptimization with Constrained RLHF
Ted Moskovitz, Aaditya K. Singh, DJ Strouse +4
Large language models are typically aligned with human preferences by optimizing (RMs) fitted to human feedback. However, human preferences are multi-facet…
cs.LG2022★ 10 cited
In-context Reinforcement Learning with Algorithm Distillation
Michael Laskin, Luyu Wang, Junhyuk Oh +11
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal seque…
cs.LG2018
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes +5
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over…