166 citations · 308 across the 15 of their papers we have counts for
4 papers · 1 filter
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning
Norman Di Palo, Leonard Hasenclever, Jan Humplik +1
We introduce Diffusion Augmented Agents (DAAG), a novel framework that leverages large language models, vision language models, and diffusion models to improve sample efficiency an…
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier +13
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common a…
Importance Weighted Policy Learning and Adaptation
Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins +5
The ability to exploit prior experience to solve novel problems rapidly is a hallmark of biological learning systems and of great practical importance for artificial ones. In the m…
Meta reinforcement learning as task inference
Jan Humplik, Alexandre Galashov, Leonard Hasenclever +3
Humans achieve efficient learning by relying on prior knowledge about the structure of naturally occurring tasks. There is considerable interest in designing reinforcement learning…