Search Results for author: Yotam Doron

Found 7 papers, 4 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 reinforcement-learning

Transformation-based Adversarial Video Prediction on Large-Scale Data

no code implementations9 Mar 2020 Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, Karen Simonyan

Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world video.

Video Generation Video Prediction

Deep Reinforcement Learning and the Deadly Triad

no code implementations6 Dec 2018 Hado van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, Joseph Modayil

In this work, we investigate the impact of the deadly triad in practice, in the context of a family of popular deep reinforcement learning models - deep Q-networks trained with experience replay - analysing how the components of this system play a role in the emergence of the deadly triad, and in the agent's performance

Learning Theory reinforcement-learning

DeepMind Control Suite

4 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 reinforcement-learning

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