91 citations · 301 across the 27 of their papers we have counts for
5 papers · 1 filter
One-shot World Models Using a Transformer Trained on a Synthetic Prior
Fabio Ferreira, Moreno Schlageter, Raghu Rajan +2
A World Model is a compressed spatial and temporal representation of a real world environment that allows one to train an agent or execute planning methods. However, world models a…
CANDID DAC: Leveraging Coupled Action Dimensions with Importance Differences in DAC
Philipp Bordne, M. Asif Hasan, Eddie Bergman +2
High-dimensional action spaces remain a challenge for dynamic algorithm configuration (DAC). Interdependencies and varying importance between action dimensions are further known ke…
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning
Tidiane Camaret Ndir, André Biedenkapp, Noor Awad
In this work, we address the challenge of zero-shot generalization (ZSG) in Reinforcement Learning (RL), where agents must adapt to entirely novel environments without additional t…
Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization
Sai Prasanna, Karim Farid, Raghu Rajan +1
Zero-shot generalization (ZSG) to unseen dynamics is a major challenge for creating generally capable embodied agents. To address the broader challenge, we start with the simpler s…
Hierarchical Transformers are Efficient Meta-Reinforcement Learners
Gresa Shala, André Biedenkapp, Josif Grabocka
We introduce Hierarchical Transformers for Meta-Reinforcement Learning (HTrMRL), a powerful online meta-reinforcement learning approach. HTrMRL aims to address the challenge of ena…