Reinforcement Learning
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference
Given time series data, how can we answer questions like "what will happen in the future?" and "how did we get here?"
Diffusion for World Modeling: Visual Details Matter in Atari Eloi Alonso University of Geneva Adam Jelley
World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner.
Interactive Visual Reasoning under Uncertainty
One of the fundamental cognitive abilities of humans is to quickly resolve uncertainty by generating hypotheses and testing them via active trials.
GT A: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning
Offline Reinforcement Learning (Offline RL) presents challenges of learning effective decision-making policies from static datasets without any online interactions.
Reining Generalization in Offline Reinforcement Learning via Representation Distinction
Offline Reinforcement Learning (RL) aims to address the challenge of distribution shift between the dataset and the learned policy, where the value of out-of-distribution (OOD) data may be erroneously estimated due to overgeneralization.
