Martin Riedmiller

research

∙ 09/14/2023

Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning

We present a novel approach to address the challenge of generalization i...

0 Cristina Pinneri, et al. ∙

research

∙ 08/29/2023

Policy composition in reinforcement learning via multi-objective policy optimization

We enable reinforcement learning agents to learn successful behavior pol...

0 Shruti Mishra, et al. ∙

research

∙ 08/15/2023

Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World

Experimentation on real robots is demanding in terms of time and costs. ...

0 Nico Gürtler, et al. ∙

research

∙ 07/21/2023

Towards practical reinforcement learning for tokamak magnetic control

Reinforcement learning (RL) has shown promising results for real-time co...

0 Brendan D. Tracey, et al. ∙

research

∙ 07/18/2023

Towards A Unified Agent with Foundation Models

Language Models and Vision Language Models have recently demonstrated un...

0 Norman Di Palo, et al. ∙

research

∙ 06/20/2023

RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation

The ability to leverage heterogeneous robotic experience from different ...

0 Konstantinos Bousmalis, et al. ∙

research

∙ 05/18/2023

A Generalist Dynamics Model for Control

We investigate the use of transformer sequence models as dynamics models...

0 Ingmar Schubert, et al. ∙

research

∙ 02/24/2023

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains

In this paper we study the problem of learning multi-step dynamics predi...

0 Jingwei Zhang, et al. ∙

research

∙ 11/24/2022

SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration

The ability to effectively reuse prior knowledge is a key requirement wh...

0 Giulia Vezzani, et al. ∙

research

∙ 10/22/2022

Solving Continuous Control via Q-learning

While there has been substantial success in applying actor-critic method...

0 Tim Seyde, et al. ∙

research

∙ 09/05/2022

MO2: Model-Based Offline Options

The ability to discover useful behaviours from past experience and trans...

0 Sasha Salter, et al. ∙

research

∙ 04/21/2022

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach

Actor-critic algorithms that make use of distributional policy evaluatio...

0 Bobak Shahriari, et al. ∙

research

∙ 01/27/2022

The Challenges of Exploration for Offline Reinforcement Learning

Offline Reinforcement Learning (ORL) enablesus to separately study the t...

0 Nathan Lambert, et al. ∙

research

∙ 11/03/2021

Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies

Reinforcement learning (RL) for continuous control typically employs dis...

7 Tim Seyde, et al. ∙

research

∙ 10/12/2021

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

We study the problem of robotic stacking with objects of complex geometr...

0 Alex X. Lee, et al. ∙

research

∙ 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. ∙

research

∙ 09/17/2021

Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration

Curiosity-based reward schemes can present powerful exploration mechanis...

0 Oliver Groth, et al. ∙

research

∙ 08/23/2021

Collect Infer – a fresh look at data-efficient Reinforcement Learning

This position paper proposes a fresh look at Reinforcement Learning (RL)...

0 Martin Riedmiller, et al. ∙

research

∙ 06/15/2021

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning

Many advances that have improved the robustness and efficiency of deep r...

0 Abbas Abdolmaleki, et al. ∙

research

∙ 01/23/2021

Rethinking Exploration for Sample-Efficient Policy Learning

Off-policy reinforcement learning for control has made great strides in ...

0 William F. Whitney, et al. ∙

research

∙ 11/03/2020

Representation Matters: Improving Perception and Exploration for Robotics

Projecting high-dimensional environment observations into lower-dimensio...

0 Markus Wulfmeier, et al. ∙

research

∙ 10/29/2020

"What, not how": Solving an under-actuated insertion task from scratch

Robot manipulation requires a complex set of skills that need to be care...

0 Giulia Vezzani, et al. ∙

research

∙ 10/20/2020

Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification

Many real-world physical control systems are required to satisfy constra...

0 Daniel J. Mankowitz, et al. ∙

research

∙ 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. ∙

research

∙ 08/06/2020

Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion

Modern Reinforcement Learning (RL) algorithms promise to solve difficult...

0 Roland Hafner, et al. ∙

research

∙ 07/30/2020

Data-efficient Hindsight Off-policy Option Learning

Solutions to most complex tasks can be decomposed into simpler, intermed...

38 Markus Wulfmeier, et al. ∙

research

∙ 05/15/2020

Simple Sensor Intentions for Exploration

Modern reinforcement learning algorithms can learn solutions to increasi...

2 Tim Hertweck, et al. ∙

research

∙ 05/15/2020

A Distributional View on Multi-Objective Policy Optimization

Many real-world problems require trading off multiple competing objectiv...

0 Abbas Abdolmaleki, et al. ∙

research

∙ 02/19/2020

Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning

Off-policy reinforcement learning algorithms promise to be applicable in...

7 Noah Y. Siegel, et al. ∙

research

∙ 01/02/2020

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Many real-world control problems involve both discrete decision variable...

15 Michael Neunert, et al. ∙

research

∙ 11/05/2019

Quinoa: a Q-function You Infer Normalized Over Actions

We present an algorithm for learning an approximate action-value soft Q-...

20 Jonas Degrave, et al. ∙

research

∙ 10/09/2019

Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models

Humans are masters at quickly learning many complex tasks, relying on an...

5 Arunkumar Byravan, et al. ∙

research

∙ 09/26/2019

V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control

Some of the most successful applications of deep reinforcement learning ...

0 H. Francis Song, et al. ∙

research

∙ 06/26/2019

Regularized Hierarchical Policies for Compositional Transfer in Robotics

The successful application of flexible, general learning algorithms -- s...

1 Markus Wulfmeier, et al. ∙

research

∙ 06/18/2019

Robust Reinforcement Learning for Continuous Control with Model Misspecification

We provide a framework for incorporating robustness -- to perturbations ...

0 Daniel J. Mankowitz, et al. ∙

research

∙ 02/13/2019

Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup

We present a method for fast training of vision based control policies o...

2 Devin Schwab, et al. ∙

research

∙ 01/03/2019

Self-supervised Learning of Image Embedding for Continuous Control

Operating directly from raw high dimensional sensory inputs like images ...

0 Carlos Florensa, et al. ∙

research

∙ 12/05/2018

Relative Entropy Regularized Policy Iteration

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

2 Abbas Abdolmaleki, et al. ∙

research

∙ 06/14/2018

Maximum a Posteriori Policy Optimisation

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

0 Abbas Abdolmaleki, et al. ∙

research

∙ 06/04/2018

Graph networks as learnable physics engines for inference and control

Understanding and interacting with everyday physical scenes requires ric...

2 Alvaro Sanchez-Gonzalez, et al. ∙

research

∙ 02/28/2018

Learning by Playing - Solving Sparse Reward Tasks from Scratch

We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm ...

0 Martin Riedmiller, et al. ∙

research

∙ 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. ∙

research

∙ 07/27/2017

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

We propose a general and model-free approach for Reinforcement Learning ...

0 Matej Večerík, et al. ∙

research

∙ 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. ∙

research

∙ 05/27/2017

PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations

We propose position-velocity encoders (PVEs) which learn---without super...

0 Rico Jonschkowski, et al. ∙

research

∙ 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. ∙

research

∙ 07/24/2015

Multimodal Deep Learning for Robust RGB-D Object Recognition

Robust object recognition is a crucial ingredient of many, if not all, r...

0 Andreas Eitel, et al. ∙

research

∙ 06/24/2015

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images

We introduce Embed to Control (E2C), a method for model learning and con...

0 Manuel Watter, et al. ∙

research

∙ 12/21/2014

Striving for Simplicity: The All Convolutional Net

Most modern convolutional neural networks (CNNs) used for object recogni...

0 Jost Tobias Springenberg, et al. ∙

research

∙ 06/26/2014

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Deep convolutional networks have proven to be very successful in learnin...

0 Alexey Dosovitskiy, et al. ∙

Martin Riedmiller

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