gradSim: Differentiable simulation for system identification and visuomotor control

no code implementations • ICLR 2021 • Krishna Murthy Jatavallabhula, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jerome Parent-Levesque, Kevin Xie, Kenny Erleben, Liam Paull, Florian Shkurti, Derek Nowrouzezahrai, Sanja Fidler

We consider the problem of estimating an object's physical properties such as mass, friction, and elasticity directly from video sequences.

Friction

Multi-objective training of Generative Adversarial Networks with multiple discriminators

1 code implementation • ICLR 2019 • Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas

Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary.

Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting

no code implementations • 2 Jun 2018 • Sai Krishna G. V., Kyle Goyette, Ahmad Chamseddine, Breandan Considine

An almost-perfect chess playing agent has been a long standing challenge in the field of Artificial Intelligence.

reinforcement-learning Reinforcement Learning (RL)