254 citations · 312 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
Semantic Exploration from Language Abstractions and Pretrained Representations
Allison C. Tam, Neil C. Rabinowitz, Andrew K. Lampinen +6
Effective exploration is a challenge in reinforcement learning (RL). Novelty-based exploration methods can suffer in high-dimensional state spaces, such as continuous partially-obs…
Collaborating with Humans without Human Data
DJ Strouse, Kevin R. McKee, Matt Botvinick +2
Collaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement learning tech…
Learning more skills through optimistic exploration
DJ Strouse, Kate Baumli, David Warde-Farley +2
Unsupervised skill learning objectives (Gregor et al., 2016, Eysenbach et al., 2018) allow agents to learn rich repertoires of behavior in the absence of extrinsic rewards. They wo…
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…