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09/14/2023
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning
We present a novel approach to address the challenge of generalization i...
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08/29/2023
Policy composition in reinforcement learning via multi-objective policy optimization
We enable reinforcement learning agents to learn successful behavior pol...
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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. ...
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07/21/2023
Towards practical reinforcement learning for tokamak magnetic control
Reinforcement learning (RL) has shown promising results for real-time co...
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07/18/2023
Towards A Unified Agent with Foundation Models
Language Models and Vision Language Models have recently demonstrated un...
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06/20/2023
RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation
The ability to leverage heterogeneous robotic experience from different ...
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05/18/2023
A Generalist Dynamics Model for Control
We investigate the use of transformer sequence models as dynamics models...
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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...
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11/24/2022
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
The ability to effectively reuse prior knowledge is a key requirement wh...
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10/22/2022
Solving Continuous Control via Q-learning
While there has been substantial success in applying actor-critic method...
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09/05/2022
MO2: Model-Based Offline Options
The ability to discover useful behaviours from past experience and trans...
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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...
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01/27/2022
The Challenges of Exploration for Offline Reinforcement Learning
Offline Reinforcement Learning (ORL) enablesus to separately study the t...
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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...
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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...
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10/07/2021
Evaluating model-based planning and planner amortization for continuous control
There is a widespread intuition that model-based control methods should ...
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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...
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08/23/2021
Collect Infer – a fresh look at data-efficient Reinforcement Learning
This position paper proposes a fresh look at Reinforcement Learning (RL)...
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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...
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01/23/2021
Rethinking Exploration for Sample-Efficient Policy Learning
Off-policy reinforcement learning for control has made great strides in ...
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11/03/2020
Representation Matters: Improving Perception and Exploration for Robotics
Projecting high-dimensional environment observations into lower-dimensio...
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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...
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10/20/2020
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
Many real-world physical control systems are required to satisfy constra...
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10/12/2020
Local Search for Policy Iteration in Continuous Control
We present an algorithm for local, regularized, policy improvement in re...
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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...
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07/30/2020
Data-efficient Hindsight Off-policy Option Learning
Solutions to most complex tasks can be decomposed into simpler, intermed...
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05/15/2020
Simple Sensor Intentions for Exploration
Modern reinforcement learning algorithms can learn solutions to increasi...
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05/15/2020
A Distributional View on Multi-Objective Policy Optimization
Many real-world problems require trading off multiple competing objectiv...
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02/19/2020
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning
Off-policy reinforcement learning algorithms promise to be applicable in...
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01/02/2020
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
Many real-world control problems involve both discrete decision variable...
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11/05/2019
Quinoa: a Q-function You Infer Normalized Over Actions
We present an algorithm for learning an approximate action-value soft Q-...
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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...
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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 ...
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06/26/2019
Regularized Hierarchical Policies for Compositional Transfer in Robotics
The successful application of flexible, general learning algorithms -- s...
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06/18/2019
Robust Reinforcement Learning for Continuous Control with Model Misspecification
We provide a framework for incorporating robustness -- to perturbations ...
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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...
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01/03/2019
Self-supervised Learning of Image Embedding for Continuous Control
Operating directly from raw high dimensional sensory inputs like images ...
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12/05/2018
Relative Entropy Regularized Policy Iteration
We present an off-policy actor-critic algorithm for Reinforcement Learni...
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06/14/2018
Maximum a Posteriori Policy Optimisation
We introduce a new algorithm for reinforcement learning called Maximum a...
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06/04/2018
Graph networks as learnable physics engines for inference and control
Understanding and interacting with everyday physical scenes requires ric...
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02/28/2018
Learning by Playing - Solving Sparse Reward Tasks from Scratch
We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm ...
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01/02/2018
DeepMind Control Suite
The DeepMind Control Suite is a set of continuous control tasks with a s...
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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 ...
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07/07/2017
Emergence of Locomotion Behaviours in Rich Environments
The reinforcement learning paradigm allows, in principle, for complex be...
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05/27/2017
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
We propose position-velocity encoders (PVEs) which learn---without super...
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10/17/2016
Learning and Transfer of Modulated Locomotor Controllers
We study a novel architecture and training procedure for locomotion task...
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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...
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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...
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12/21/2014
Striving for Simplicity: The All Convolutional Net
Most modern convolutional neural networks (CNNs) used for object recogni...
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06/26/2014