5 citations · 7 across the 5 of their papers we have counts for
5 papers
Rejecting Hallucinated State Targets during Planning
Mingde Zhao, Tristan Sylvain, Romain Laroche +2
In planning processes of computational decision-making agents, generative or predictive models are often used as "generators" to propose "targets" representing sets of expected or…
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik +6
Offline policy learning is aimed at learning decision-making policies using existing datasets of trajectories without collecting additional data. The primary motivation for using r…
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Mingde Zhao, Safa Alver, Harm van Seijen +3
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situat…
Think Before You Act: Decision Transformers with Working Memory
Jikun Kang, Romain Laroche, Xingdi Yuan +3
Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance relies on massive data and computation. We…
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
Yuchen Lu, Zhen Liu, Aristide Baratin +3
We address the problem of evaluating the quality of self-supervised learning (SSL) models without access to supervised labels, while being agnostic to the architecture, learning al…