2 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation
Lucas Maes, Quentin Le Lidec, Luiz Facury +9
World models are central to building agents that can reason, plan, and generalize beyond their training data. However, research on world models is currently fragmented, with dispar…
Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling
Tal Daniel, Carl Qi, Dan Haramati +5
We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM aut…
stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
Lucas Maes, Quentin Le Lidec, Dan Haramati +4
World Models have emerged as a powerful paradigm for learning compact, predictive representations of environment dynamics, enabling agents to reason, plan, and generalize beyond di…
Hierarchical Entity-centric Reinforcement Learning with Factored Subgoal Diffusion
Dan Haramati, Carl Qi, Tal Daniel +3
We propose a hierarchical entity-centric framework for offline Goal-Conditioned Reinforcement Learning (GCRL) that combines subgoal decomposition with factored structure to solve l…