164 citations · 589 across the 14 of their papers we have counts for
3 papers · 1 filter
Entity Abstraction in Visual Model-Based Reinforcement Learning
Rishi Veerapaneni, John D. Co-Reyes, Michael Chang +5
This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit i…
When to Trust Your Model: Model-Based Policy Optimization
Michael Janner, Justin Fu, Marvin Zhang +1
Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data. In this…
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning
Michael Janner, Sergey Levine, William T. Freeman +3
Object-based factorizations provide a useful level of abstraction for interacting with the world. Building explicit object representations, however, often requires supervisory sign…