Learning rigid dynamics with face interaction graph networks

no code implementations • 7 Dec 2022 • Kelsey R. Allen, Yulia Rubanova, Tatiana Lopez-Guevara, William Whitney, Alvaro Sanchez-Gonzalez, Peter Battaglia, Tobias Pfaff

Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions.

The Challenges of Exploration for Offline Reinforcement Learning

no code implementations • 27 Jan 2022 • Nathan Lambert, Markus Wulfmeier, William Whitney, Arunkumar Byravan, Michael Bloesch, Vibhavari Dasagi, Tim Hertweck, Martin Riedmiller

Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal behaviour.

Model Predictive Control Offline RL +2

Dynamics-aware Embeddings

2 code implementations • ICLR 2020 • William Whitney, Rajat Agarwal, Kyunghyun Cho, Abhinav Gupta

In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL).

Continuous Control reinforcement-learning +2

Understanding the Asymptotic Performance of Model-Based RL Methods

no code implementations • 27 Sep 2018 • William Whitney, Rob Fergus

In complex simulated environments, model-based reinforcement learning methods typically lag the asymptotic performance of model-free approaches.

Model-based Reinforcement Learning

Disentangled Representations in Neural Models

no code implementations • 7 Feb 2016 • William Whitney

In this paper, I describe methods for learning disentangled representations in the two domains of graphics and computation.

Representation Learning