1 code implementation • 15 Sep 2022 • Sahand Rezaei-Shoshtari, Rosie Zhao, Prakash Panangaden, David Meger, Doina Precup
Abstraction has been widely studied as a way to improve the efficiency and generalization of reinforcement learning algorithms.
2 code implementations • 9 May 2023 • Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup
Our policy gradient results allow for leveraging approximate symmetries of the environment for policy optimization.
1 code implementation • 12 Jan 2021 • Sahand Rezaei-Shoshtari, Francois Robert Hogan, Michael Jenkin, David Meger, Gregory Dudek
Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelligent and anticipatory actions.
1 code implementation • 22 Jul 2020 • Sahand Rezaei-Shoshtari, David Meger, Inna Sharf
Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work.
no code implementations • 5 Oct 2019 • Sahand Rezaei-Shoshtari, David Meger, Inna Sharf
Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators.
no code implementations • 28 Nov 2022 • Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois Robert Hogan, Gregory Dudek, David Meger
In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks.