Nicolas Heess

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PhD student at the Institute for Adaptive and Neural Computation, University of Edinburgh

Exploiting Hierarchy for Learning and Transfer in KL-regularized RL

As reinforcement learning agents are tasked with solving more challengin...
03/18/2019 ∙ by Dhruva Tirumala, et al. ∙ 20 ∙ share

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Quinoa: a Q-function You Infer Normalized Over Actions

We present an algorithm for learning an approximate action-value soft Q-...
11/05/2019 ∙ by Jonas Degrave, et al. ∙ 20 ∙ share

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A Generalized Training Approach for Multiagent Learning

This paper investigates a population-based training regime based on game...
09/27/2019 ∙ by Paul Müller, et al. ∙ 20 ∙ share

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Value-driven Hindsight Modelling

Value estimation is a critical component of the reinforcement learning (...
02/19/2020 ∙ by Arthur Guez, et al. ∙ 17 ∙ share

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Meta-learning of Sequential Strategies

In this report we review memory-based meta-learning as a tool for buildi...
05/08/2019 ∙ by Pedro A. Ortega, et al. ∙ 16 ∙ share

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Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Many real-world control problems involve both discrete decision variable...
01/02/2020 ∙ by Michael Neunert, et al. ∙ 15 ∙ share

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Neural probabilistic motor primitives for humanoid control

We focus on the problem of learning a single motor module that can flexi...
11/28/2018 ∙ by Josh Merel, et al. ∙ 12 ∙ share

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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...
11/15/2019 ∙ by Josh Merel, et al. ∙ 11 ∙ share

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The Termination Critic

In this work, we consider the problem of autonomously discovering behavi...
02/26/2019 ∙ by Anna Harutyunyan, et al. ∙ 10 ∙ share

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Stabilizing Transformers for Reinforcement Learning

Owing to their ability to both effectively integrate information over lo...
10/13/2019 ∙ by Emilio Parisotto, et al. ∙ 7 ∙ share

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Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures

This paper addresses the problem of evaluating learning systems in safet...
12/04/2018 ∙ by Jonathan Uesato, et al. ∙ 6 ∙ share

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Information asymmetry in KL-regularized RL

Many real world tasks exhibit rich structure that is repeated across dif...
05/03/2019 ∙ by Alexandre Galashov, et al. ∙ 6 ∙ share

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Meta reinforcement learning as task inference

Humans achieve efficient learning by relying on prior knowledge about th...
05/15/2019 ∙ by Jan Humplik, et al. ∙ 5 ∙ share

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

Humans are masters at quickly learning many complex tasks, relying on an...
10/09/2019 ∙ by Arunkumar Byravan, et al. ∙ 5 ∙ share

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Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces

Direct optimization is an appealing approach to differentiating through ...
06/14/2019 ∙ by Guy Lorberbom, et al. ∙ 3 ∙ share

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Mix&Match - Agent Curricula for Reinforcement Learning

We introduce Mix&Match (M&M) - a training framework designed to facilita...
06/05/2018 ∙ by Wojciech Marian Czarnecki, et al. ∙ 2 ∙ share

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Graph networks as learnable physics engines for inference and control

Understanding and interacting with everyday physical scenes requires ric...
06/04/2018 ∙ by Alvaro Sanchez-Gonzalez, et al. ∙ 2 ∙ share

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Relative Entropy Regularized Policy Iteration

We present an off-policy actor-critic algorithm for Reinforcement Learni...
12/05/2018 ∙ by Abbas Abdolmaleki, et al. ∙ 2 ∙ share

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Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction

Deep reinforcement learning (RL) algorithms have made great strides in r...
12/05/2018 ∙ by Jonathan J Hunt, et al. ∙ 2 ∙ share

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Emergent Coordination Through Competition

We study the emergence of cooperative behaviors in reinforcement learnin...
02/19/2019 ∙ by Siqi Liu, et al. ∙ 2 ∙ share

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Regularized Hierarchical Policies for Compositional Transfer in Robotics

The successful application of flexible, general learning algorithms -- s...
06/26/2019 ∙ by Markus Wulfmeier, et al. ∙ 1 ∙ share

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

We propose a general and model-free approach for Reinforcement Learning ...
07/27/2017 ∙ by Matej Večerík, et al. ∙ 0 ∙ share

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Emergence of Locomotion Behaviours in Rich Environments

The reinforcement learning paradigm allows, in principle, for complex be...
07/07/2017 ∙ by Nicolas Heess, et al. ∙ 0 ∙ share

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Learning Hierarchical Information Flow with Recurrent Neural Modules

We propose ThalNet, a deep learning model inspired by neocortical commun...
06/18/2017 ∙ by Danijar Hafner, et al. ∙ 0 ∙ share

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Imagination-Augmented Agents for Deep Reinforcement Learning

We introduce Imagination-Augmented Agents (I2As), a novel architecture f...
07/19/2017 ∙ by Theophane Weber, et al. ∙ 0 ∙ share

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Learning model-based planning from scratch

Conventional wisdom holds that model-based planning is a powerful approa...
07/19/2017 ∙ by Razvan Pascanu, et al. ∙ 0 ∙ share

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Metacontrol for Adaptive Imagination-Based Optimization

Many machine learning systems are built to solve the hardest examples of...
05/07/2017 ∙ by Jessica B. Hamrick, et al. ∙ 0 ∙ share

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Distral: Robust Multitask Reinforcement Learning

Most deep reinforcement learning algorithms are data inefficient in comp...
07/13/2017 ∙ by Yee Whye Teh, et al. ∙ 0 ∙ share

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Learning Continuous Control Policies by Stochastic Value Gradients

We present a unified framework for learning continuous control policies ...
10/30/2015 ∙ by Nicolas Heess, et al. ∙ 0 ∙ share

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Filtering Variational Objectives

When used as a surrogate objective for maximum likelihood estimation in ...
05/25/2017 ∙ by Chris J. Maddison, et al. ∙ 0 ∙ share

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Learning and Transfer of Modulated Locomotor Controllers

We study a novel architecture and training procedure for locomotion task...
10/17/2016 ∙ by Nicolas Heess, et al. ∙ 0 ∙ share

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Continuous control with deep reinforcement learning

We adapt the ideas underlying the success of Deep Q-Learning to the cont...
09/09/2015 ∙ by Timothy P. Lillicrap, et al. ∙ 0 ∙ share

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Unsupervised Learning of 3D Structure from Images

A key goal of computer vision is to recover the underlying 3D structure ...
07/03/2016 ∙ by Danilo Jimenez Rezende, et al. ∙ 0 ∙ share

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Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

We propose an efficient nonparametric strategy for learning a message op...
03/09/2015 ∙ by Wittawat Jitkrittum, et al. ∙ 0 ∙ share

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Passing Expectation Propagation Messages with Kernel Methods

We propose to learn a kernel-based message operator which takes as input...
01/02/2015 ∙ by Wittawat Jitkrittum, et al. ∙ 0 ∙ share

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Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

We present a framework for efficient inference in structured image model...
03/28/2016 ∙ by S. M. Ali Eslami, et al. ∙ 0 ∙ share

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Recurrent Models of Visual Attention

Applying convolutional neural networks to large images is computationall...
06/24/2014 ∙ by Volodymyr Mnih, et al. ∙ 0 ∙ share

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Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs

We propose an extension of the Restricted Boltzmann Machine (RBM) that a...
07/19/2011 ∙ by Nicolas Heess, et al. ∙ 0 ∙ share

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Reinforcement and Imitation Learning for Diverse Visuomotor Skills

We propose a model-free deep reinforcement learning method that leverage...
02/26/2018 ∙ by Yuke Zhu, et al. ∙ 0 ∙ share

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Learning by Playing - Solving Sparse Reward Tasks from Scratch

We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm ...
02/28/2018 ∙ by Martin Riedmiller, et al. ∙ 0 ∙ share

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Distributed Distributional Deterministic Policy Gradients

This work adopts the very successful distributional perspective on reinf...
04/23/2018 ∙ by Gabriel Barth-Maron, et al. ∙ 0 ∙ share

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Relational inductive biases, deep learning, and graph networks

Artificial intelligence (AI) has undergone a renaissance recently, makin...
06/04/2018 ∙ by Peter W. Battaglia, et al. ∙ 0 ∙ share

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Maximum a Posteriori Policy Optimisation

We introduce a new algorithm for reinforcement learning called Maximum a...
06/14/2018 ∙ by Abbas Abdolmaleki, et al. ∙ 0 ∙ share

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Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

Learning policies on data synthesized by models can in principle quench ...
11/15/2018 ∙ by Lars Buesing, et al. ∙ 0 ∙ share

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Hierarchical visuomotor control of humanoids

We aim to build complex humanoid agents that integrate perception, motor...
11/23/2018 ∙ by Josh Merel, et al. ∙ 0 ∙ share

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Credit Assignment Techniques in Stochastic Computation Graphs

Stochastic computation graphs (SCGs) provide a formalism to represent st...
01/07/2019 ∙ by Theophane Weber, et al. ∙ 0 ∙ share

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Value constrained model-free continuous control

The naive application of Reinforcement Learning algorithms to continuous...
02/12/2019 ∙ by Steven Bohez, et al. ∙ 0 ∙ share

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Self-supervised Learning of Image Embedding for Continuous Control

Operating directly from raw high dimensional sensory inputs like images ...
01/03/2019 ∙ by Carlos Florensa, et al. ∙ 0 ∙ share

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

Some of the most successful applications of deep reinforcement learning ...
09/26/2019 ∙ by H. Francis Song, et al. ∙ 0 ∙ share

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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...
10/15/2019 ∙ by Lars Buesing, et al. ∙ 0 ∙ share

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Nicolas Heess

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