b'Nicolas Heess'

research

∙ 05/31/2022

Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games

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

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 ...

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...

Dianbo Liu, et al. ∙

research

∙ 05/12/2022

A Generalist Agent

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

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...

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,...

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...

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...

Steven Bohez, et al. ∙

research

∙ 02/17/2022

Retrieval-Augmented Reinforcement Learning

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

Anirudh Goyal, et al. ∙

research

∙ 02/15/2022

NeuPL: Neural Population Learning

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

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...

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...

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...

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 ...

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...

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...

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)...

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...

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...

Siqi Liu, et al. ∙

research

∙ 03/02/2021

Neural Production Systems

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

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 ...

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 ...

Karl Tuyls, et al. ∙

research

∙ 10/27/2020

Behavior Priors for Efficient Reinforcement Learning

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

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...

Daniel J. Mankowitz, et al. ∙

research

∙ 10/16/2020

Learning Dexterous Manipulation from Suboptimal Experts

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

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...

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...

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...

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 ...

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 ...

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...

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...

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...

Markus Wulfmeier, et al. ∙

research

∙ 06/26/2020

Critic Regularized Regression

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

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...

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 ...

Yuval Tassa, et al. ∙

research

∙ 05/15/2020

Simple Sensor Intentions for Exploration

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

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...

Abbas Abdolmaleki, et al. ∙

research

∙ 04/23/2020

Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning

Standard planners for sequential decision making (including Monte Carlo ...

Giambattista Parascandolo, et al. ∙

research

∙ 02/19/2020

Value-driven Hindsight Modelling

Value estimation is a critical component of the reinforcement learning (...

Arthur Guez, 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...

Michael Neunert, et al. ∙

research

∙ 12/05/2019

Hindsight Credit Assignment

We consider the problem of efficient credit assignment in reinforcement ...

Anna Harutyunyan, et al. ∙

research

∙ 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...

Josh Merel, 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-...

Jonas Degrave, et al. ∙

research

∙ 10/15/2019

Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions

A plethora of problems in AI, engineering and the sciences are naturally...

Lars Buesing, et al. ∙

research

∙ 10/13/2019

Stabilizing Transformers for Reinforcement Learning

Owing to their ability to both effectively integrate information over lo...

Emilio Parisotto, 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...

Arunkumar Byravan, et al. ∙

research

∙ 09/27/2019

A Generalized Training Approach for Multiagent Learning

This paper investigates a population-based training regime based on game...

Paul Müller, 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 ...

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...

Markus Wulfmeier, et al. ∙

Nicolas Heess

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