444 citations · 604 across the 11 of their papers we have counts for
9 papers · 1 filter
Learning rigid dynamics with face interaction graph networks
Kelsey R. Allen, Yulia Rubanova, Tatiana Lopez-Guevara +4
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GN…
Physical Design using Differentiable Learned Simulators
Kelsey R. Allen, Tatiana Lopez-Guevara, Kimberly Stachenfeld +4
Designing physical artifacts that serve a purpose - such as tools and other functional structures - is central to engineering as well as everyday human behavior. Though automating…
Constraint-based graph network simulator
Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff +1
In the area of physical simulations, nearly all neural-network-based methods directly predict future states from the input states. However, many traditional simulation engines inst…
Predicting Physics in Mesh-reduced Space with Temporal Attention
Xu Han, Han Gao, Tobias Pfaff +2
Graph-based next-step prediction models have recently been very successful in modeling complex high-dimensional physical systems on irregular meshes. However, due to their short te…
Learning Mesh-Based Simulation with Graph Networks
Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez +1
Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering. Mesh representations support powerful numerical integrat…
Learning to Simulate Complex Physics with Graph Networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff +3
Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and…
Combining Q-Learning and Search with Amortized Value Estimates
Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez +4
We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned pri…
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang +8
Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap…
Playing hard exploration games by watching YouTube
Yusuf Aytar, Tobias Pfaff, David Budden +3
Deep reinforcement learning methods traditionally struggle with tasks where environment rewards are particularly sparse. One successful method of guiding exploration in these domai…