10 citations · 40 across the 20 of their papers we have counts for
6 papers · 1 filter
Improving Transformer World Models for Data-Efficient RL
Antoine Dedieu, Joseph Ortiz, Xinghua Lou +5
We present three improvements to the standard model-based RL paradigm based on transformers: (a) "Dyna with warmup", which trains the policy on real and imaginary data, but only st…
Diffusion Model Predictive Control
Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju +6
We propose Diffusion Model Predictive Control (D-MPC), a novel MPC approach that learns a multi-step action proposal and a multi-step dynamics model, both using diffusion models, a…
DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors
Joseph Ortiz, Antoine Dedieu, Wolfgang Lehrach +7
Learning from previously collected data via behavioral cloning or offline reinforcement learning (RL) is a powerful recipe for scaling generalist agents by avoiding the need for ex…
Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments
Antoine Dedieu, Wolfgang Lehrach, Guangyao Zhou +2
Despite their stellar performance on a wide range of tasks, including in-context tasks only revealed during inference, vanilla transformers and variants trained for next-token pred…
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation
Antoine Dedieu, Guangyao Zhou, Dileep George +1
Noisy-OR Bayesian Networks (BNs) are a family of probabilistic graphical models which express rich statistical dependencies in binary data. Variational inference (VI) has been the…
Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping
Guangyao Zhou, Wolfgang Lehrach, Antoine Dedieu +2
Discrete undirected graphical models, also known as Markov Random Fields (MRFs), can flexibly encode probabilistic interactions of multiple variables, and have enjoyed successful a…