Search Results for author: Tom Erez

Found 13 papers, 6 papers with code

dm_control: Software and Tasks for Continuous Control

1 code implementation22 Jun 2020 Yuval Tassa, Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Piotr Trochim, Si-Qi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess

The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.

Continuous Control

Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks

no code implementations15 Nov 2019 Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, Nicolas Heess

We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions.

Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer

no code implementations21 Oct 2019 Rae Jeong, Jackie Kay, Francesco Romano, Thomas Lampe, Tom Rothorl, Abbas Abdolmaleki, Tom Erez, Yuval Tassa, Francesco Nori

Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system.

Learning Awareness Models

no code implementations ICLR 2018 Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world.

Reinforcement and Imitation Learning for Diverse Visuomotor Skills

1 code implementation ICLR 2018 Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess

We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent.

Imitation Learning

DeepMind Control Suite

3 code implementations2 Jan 2018 Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, Martin Riedmiller

The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents.

Continuous Control

Emergence of Locomotion Behaviours in Rich Environments

5 code implementations7 Jul 2017 Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, David Silver

The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals.

Learning to Perform Physics Experiments via Deep Reinforcement Learning

no code implementations6 Nov 2016 Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter Battaglia, Nando de Freitas

When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way.

Learning Continuous Control Policies by Stochastic Value Gradients

1 code implementation NeurIPS 2015 Nicolas Heess, Greg Wayne, David Silver, Timothy Lillicrap, Yuval Tassa, Tom Erez

One of these variants, SVG(1), shows the effectiveness of learning models, value functions, and policies simultaneously in continuous domains.

Continuous Control

Receding Horizon Differential Dynamic Programming

no code implementations NeurIPS 2007 Yuval Tassa, Tom Erez, William D. Smart

The control of high-dimensional, continuous, non-linear systems is a key problem in reinforcement learning and control.

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