Yuval Tassa | DeepAI

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∙ 06/14/2023

Language to Rewards for Robotic Skill Synthesis

Large language models (LLMs) have demonstrated exciting progress in acqu...

0 Wenhao Yu, et al. ∙

∙ 04/26/2023

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

We investigate whether Deep Reinforcement Learning (Deep RL) is able to ...

0 Tuomas Haarnoja, et al. ∙

∙ 12/01/2022

Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo

We introduce MuJoCo MPC (MJPC), an open-source, interactive application ...

0 Taylor Howell, et al. ∙

∙ 03/31/2022

Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors

We investigate the use of prior knowledge of human and animal movement t...

0 Steven Bohez, et al. ∙

∙ 12/11/2021

OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion

Muscle-actuated control is a research topic of interest spanning differe...

7 Vittorio La Barbera, et al. ∙

∙ 10/07/2021

Evaluating model-based planning and planner amortization for continuous control

There is a widespread intuition that model-based control methods should ...

0 Arunkumar Byravan, et al. ∙

∙ 09/29/2021

Learning Dynamics Models for Model Predictive Agents

Model-Based Reinforcement Learning involves learning a dynamics model fr...

0 Michael Lutter, et al. ∙

∙ 05/25/2021

From Motor Control to Team Play in Simulated Humanoid Football

Intelligent behaviour in the physical world exhibits structure at multip...

2 Siqi Liu, et al. ∙

∙ 10/12/2020

Local Search for Policy Iteration in Continuous Control

We present an algorithm for local, regularized, policy improvement in re...

0 Jost Tobias Springenberg, et al. ∙

∙ 06/22/2020

dm_control: Software and Tasks for Continuous Control

The dm_control software package is a collection of Python libraries and ...

18 Yuval Tassa, et al. ∙

∙ 11/15/2019

Reusable neural skill embeddings for vision-guided whole body movement and object manipulation

Both in simulation settings and robotics, there is an ambition to produc...

11 Josh Merel, et al. ∙

∙ 10/21/2019

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

Learning robotic control policies in the real world gives rise to challe...

0 Rae Jeong, et al. ∙

∙ 03/20/2019

Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning

Robots must know how to be gentle when they need to interact with fragil...

0 Sandy H. Huang, et al. ∙

∙ 12/05/2018

Relative Entropy Regularized Policy Iteration

We present an off-policy actor-critic algorithm for Reinforcement Learni...

2 Abbas Abdolmaleki, et al. ∙

∙ 06/14/2018

Maximum a Posteriori Policy Optimisation

We introduce a new algorithm for reinforcement learning called Maximum a...

0 Abbas Abdolmaleki, et al. ∙

∙ 04/17/2018

Learning Awareness Models

We consider the setting of an agent with a fixed body interacting with a...

0 Brandon Amos, et al. ∙

∙ 01/26/2018

Safe Exploration in Continuous Action Spaces

We address the problem of deploying a reinforcement learning (RL) agent ...

0 Gal Dalal, et al. ∙

∙ 01/02/2018

DeepMind Control Suite

The DeepMind Control Suite is a set of continuous control tasks with a s...

0 Yuval Tassa, et al. ∙

∙ 07/07/2017

Emergence of Locomotion Behaviours in Rich Environments

The reinforcement learning paradigm allows, in principle, for complex be...

0 Nicolas Heess, et al. ∙

∙ 10/17/2016

Learning and Transfer of Modulated Locomotor Controllers

We study a novel architecture and training procedure for locomotion task...

0 Nicolas Heess, et al. ∙

∙ 03/28/2016

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

We present a framework for efficient inference in structured image model...

0 S. M. Ali Eslami, et al. ∙

∙ 10/30/2015

Learning Continuous Control Policies by Stochastic Value Gradients

We present a unified framework for learning continuous control policies ...

0 Nicolas Heess, et al. ∙

∙ 09/09/2015

Continuous control with deep reinforcement learning

We adapt the ideas underlying the success of Deep Q-Learning to the cont...

0 Timothy P. Lillicrap, et al. ∙

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