294 citations · 1.2k across the 24 of their papers we have counts for
24 papers
RoboVQA: Multimodal Long-Horizon Reasoning for Robotics
Pierre Sermanet, Tianli Ding, Jeffrey Zhao +18
We present a scalable, bottom-up and intrinsically diverse data collection scheme that can be used for high-level reasoning with long and medium horizons and that has 2.2x higher t…
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
Ken Caluwaerts, Atil Iscen, J. Chase Kew +41
Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biol…
Get Back Here: Robust Imitation by Return-to-Distribution Planning
Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi +6
We consider the Imitation Learning (IL) setup where expert data are not collected on the actual deployment environment but on a different version. To address the resulting distribu…
Investigating the role of model-based learning in exploration and transfer
Jacob Walker, Eszter Vértes, Yazhe Li +4
State of the art reinforcement learning has enabled training agents on tasks of ever increasing complexity. However, the current paradigm tends to favor training agents from scratc…
Learning Reward Functions for Robotic Manipulation by Observing Humans
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal +2
Observing a human demonstrator manipulate objects provides a rich, scalable and inexpensive source of data for learning robotic policies. However, transferring skills from human vi…
On the importance of data collection for training general goal-reaching policies
Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold +3
Recent advances in ML suggest that the quantity of data available to a model is one of the primary bottlenecks to high performance. Although for language-based tasks there exist al…
Learning Dynamics Models for Model Predictive Agents
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan +5
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner…
Residual Reinforcement Learning from Demonstrations
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal +2
Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a…
Learning to run a Power Network Challenge: a Retrospective Analysis
Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold +7
Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand…
A Geometric Perspective on Self-Supervised Policy Adaptation
Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold +1
One of the most challenging aspects of real-world reinforcement learning (RL) is the multitude of unpredictable and ever-changing distractions that could divert an agent from what…
Model-Based Offline Planning
Arthur Argenson, Gabriel Dulac-Arnold
Offline learning is a key part of making reinforcement learning (RL) useable in real systems. Offline RL looks at scenarios where there is data from a system's operation, but no di…
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov +15
Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to lea…
An empirical investigation of the challenges of real-world reinforcement learning
Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz +4
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research a…
Differentiable Deep Clustering with Cluster Size Constraints
Aude Genevay, Gabriel Dulac-Arnold, Jean-Philippe Vert
Clustering is a fundamental unsupervised learning approach. Many clustering algorithms -- such as -means -- rely on the euclidean distance as a similarity measure, which is ofte…
Deep multi-class learning from label proportions
Gabriel Dulac-Arnold, Neil Zeghidour, Marco Cuturi +2
We propose a learning algorithm capable of learning from label proportions instead of direct data labels. In this scenario, our data are arranged into various bags of a certain siz…
Challenges of Real-World Reinforcement Learning
Gabriel Dulac-Arnold, Daniel Mankowitz, Todd Hester
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research a…
Deep Q-learning from Demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin +11
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of dat…
The Predictron: End-To-End Learning and Planning
David Silver, Hado van Hasselt, Matteo Hessel +8
One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning. In this document we introduce the predictron architecture. Th…
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold +3
Many real-world problems come with action spaces represented as feature vectors. Although high-dimensional control is a largely unsolved problem, there has recently been progress f…
Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt +7
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, i…
Sequentially Generated Instance-Dependent Image Representations for Classification
Gabriel Dulac-Arnold, Ludovic Denoyer, Nicolas Thome +2
In this paper, we investigate a new framework for image classification that adaptively generates spatial representations. Our strategy is based on a sequential process that learns…
Fast Reinforcement Learning with Large Action Sets using Error-Correcting Output Codes for MDP Factorization
Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux +1
The use of Reinforcement Learning in real-world scenarios is strongly limited by issues of scale. Most RL learning algorithms are unable to deal with problems composed of hundreds…
Datum-Wise Classification: A Sequential Approach to Sparsity
Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux +1
We propose a novel classification technique whose aim is to select an appropriate representation for each datapoint, in contrast to the usual approach of selecting a representation…
Text Classification: A Sequential Reading Approach
Gabriel Dulac-Arnold, Ludovic Denoyer, Patrick Gallinari
We propose to model the text classification process as a sequential decision process. In this process, an agent learns to classify documents into topics while reading the document…