b'Nicolas Heess'

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

∙ 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

∙ 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. ∙

research

∙ 05/24/2023

Barkour: Benchmarking Animal-level Agility with Quadruped Robots

Animals have evolved various agile locomotion strategies, such as sprint...

2 Ken Caluwaerts, 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

∙ 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. ∙

research

∙ 04/13/2023

Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation

Recent works have shown that large models pretrained on common visual le...

1 Mohit Sharma, 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

∙ 12/28/2022

Representation Learning in Deep RL via Discrete Information Bottleneck

Several self-supervised representation learning methods have been propos...

0 Riashat Islam, 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

∙ 10/10/2022

NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields

We present a system for applying sim2real approaches to "in the wild" sc...

0 Arunkumar Byravan, et al. ∙

research

∙ 10/04/2022

Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

In cooperative multi-agent reinforcement learning, a team of agents work...

2 Dianbo Liu, 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

∙ 05/31/2022

Simplex NeuPL: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games

Learning to play optimally against any mixture over a diverse set of str...

0 Siqi Liu, et al. ∙

research

∙ 05/23/2022

Data augmentation for efficient learning from parametric experts

We present a simple, yet powerful data-augmentation technique to enable ...

0 Alexandre Galashov, et al. ∙

research

∙ 05/21/2022

Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel

In Multi-Agent Reinforcement Learning (MARL), specialized channels are o...

41 Dianbo Liu, et al. ∙

research

∙ 05/12/2022

A Generalist Agent

Inspired by progress in large-scale language modeling, we apply a simila...

12 Scott Reed, 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

∙ 04/19/2022

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation

We consider the offline constrained reinforcement learning (RL) problem,...

0 Jongmin Lee, et al. ∙

research

∙ 04/12/2022

Offline Distillation for Robot Lifelong Learning with Imbalanced Experience

Robots will experience non-stationary environment dynamics throughout th...

0 Wenxuan Zhou, et al. ∙

research

∙ 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. ∙

research

∙ 02/17/2022

Retrieval-Augmented Reinforcement Learning

Most deep reinforcement learning (RL) algorithms distill experience into...

0 Anirudh Goyal, et al. ∙

research

∙ 02/15/2022

NeuPL: Neural Population Learning

Learning in strategy games (e.g. StarCraft, poker) requires the discover...

0 Siqi Liu, et al. ∙

research

∙ 12/09/2021

Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies

For robots operating in the real world, it is desirable to learn reusabl...

0 Dushyant Rao, et al. ∙

research

∙ 10/30/2021

Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner

Dynamic quadruped locomotion over challenging terrains with precise foot...

5 Philemon Brakel, et al. ∙

research

∙ 10/08/2021

Offline Meta-Reinforcement Learning for Industrial Insertion

Reinforcement learning (RL) can in principle make it possible for robots...

0 Tony Z. Zhao, 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

∙ 09/29/2021

Learning Dynamics Models for Model Predictive Agents

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

0 Michael Lutter, 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

∙ 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. ∙

research

∙ 03/02/2021

Neural Production Systems

Visual environments are structured, consisting of distinct objects or en...

12 Anirudh Goyal, et al. ∙

research

∙ 11/18/2020

Counterfactual Credit Assignment in Model-Free Reinforcement Learning

Credit assignment in reinforcement learning is the problem of measuring ...

8 Thomas Mesnard, et al. ∙

research

∙ 11/18/2020

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning ...

11 Karl Tuyls, et al. ∙

research

∙ 10/27/2020

Behavior Priors for Efficient Reinforcement Learning

As we deploy reinforcement learning agents to solve increasingly challen...

10 Dhruva Tirumala, 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

∙ 10/16/2020

Learning Dexterous Manipulation from Suboptimal Experts

Learning dexterous manipulation in high-dimensional state-action spaces ...

0 Rae Jeong, 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

∙ 10/03/2020

Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban

Intelligent robots need to achieve abstract objectives using concrete, s...

18 Peter Karkus, et al. ∙

research

∙ 09/30/2020

Learning to swim in potential flow

Fish swim by undulating their bodies. These propulsive motions require c...

0 Yusheng Jiao, et al. ∙

research

∙ 09/11/2020

Physically Embedded Planning Problems: New Challenges for Reinforcement Learning

Recent work in deep reinforcement learning (RL) has produced algorithms ...

7 Mehdi Mirza, et al. ∙

research

∙ 09/10/2020

Importance Weighted Policy Learning and Adaption

The ability to exploit prior experience to solve novel problems rapidly ...

6 Alexandre Galashov, et al. ∙

research

∙ 09/03/2020

Action and Perception as Divergence Minimization

We introduce a unified objective for action and perception of intelligen...

10 Danijar Hafner, 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

∙ 06/26/2020

Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the ...

32 Ziyu Wang, et al. ∙

research

∙ 06/24/2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning

Offline methods for reinforcement learning have the potential to help br...

10 Caglar Gulcehre, et al. ∙

research

∙ 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. ∙

Nicolas Heess

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