Theophane Weber

research

∙ 04/12/2023

DiscoGen: Learning to Discover Gene Regulatory Networks

Accurately inferring Gene Regulatory Networks (GRNs) is a critical and c...

0 Nan Rosemary Ke, et al. ∙

research

∙ 02/09/2023

Equivariant MuZero

Deep reinforcement learning repeatedly succeeds in closed, well-defined ...

0 Andreea Deac, et al. ∙

research

∙ 02/08/2023

Investigating the role of model-based learning in exploration and transfer

State of the art reinforcement learning has enabled training agents on t...

0 Jacob Walker, et al. ∙

research

∙ 01/13/2023

Laser: Latent Set Representations for 3D Generative Modeling

NeRF provides unparalleled fidelity of novel view synthesis: rendering a...

0 Pol Moreno, et al. ∙

research

∙ 06/10/2022

Large-Scale Retrieval for Reinforcement Learning

Effective decision making involves flexibly relating past experiences an...

0 Peter C. Humphreys, et al. ∙

research

∙ 04/11/2022

Learning to Induce Causal Structure

The fundamental challenge in causal induction is to infer the underlying...

0 Nan Rosemary Ke, 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

∙ 11/02/2021

Procedural Generalization by Planning with Self-Supervised World Models

One of the key promises of model-based reinforcement learning is the abi...

4 Ankesh Anand, et al. ∙

research

∙ 04/13/2021

Muesli: Combining Improvements in Policy Optimization

We propose a novel policy update that combines regularized policy optimi...

0 Matteo Hessel, et al. ∙

research

∙ 02/24/2021

Synthetic Returns for Long-Term Credit Assignment

Since the earliest days of reinforcement learning, the workhorse method ...

6 David Raposo, et al. ∙

research

∙ 02/03/2021

Neural Recursive Belief States in Multi-Agent Reinforcement Learning

In multi-agent reinforcement learning, the problem of learning to act is...

0 Pol Moreno, et al. ∙

research

∙ 12/14/2020

A case for new neural network smoothness constraints

How sensitive should machine learning models be to input changes? We tac...

0 Mihaela Rosca, 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

∙ 11/08/2020

On the role of planning in model-based deep reinforcement learning

Model-based planning is often thought to be necessary for deep, careful ...

0 Jessica B. Hamrick, 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

∙ 09/11/2020

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

∙ 04/23/2020

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

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

27 Giambattista Parascandolo, et al. ∙

research

∙ 02/19/2020

Value-driven Hindsight Modelling

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

17 Arthur Guez, et al. ∙

research

∙ 02/07/2020

Causally Correct Partial Models for Reinforcement Learning

In reinforcement learning, we can learn a model of future observations a...

17 Danilo J. Rezende, et al. ∙

research

∙ 12/05/2019

Combining Q-Learning and Search with Amortized Value Estimates

We introduce "Search with Amortized Value Estimates" (SAVE), an approach...

0 Jessica B. Hamrick, 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...

0 Lars Buesing, et al. ∙

research

∙ 10/02/2019

Unsupervised Doodling and Painting with Improved SPIRAL

We investigate using reinforcement learning agents as generative models ...

13 John F. J. Mellor, et al. ∙

research

∙ 01/11/2019

An investigation of model-free planning

The field of reinforcement learning (RL) is facing increasingly challeng...

10 Arthur Guez, et al. ∙

research

∙ 01/07/2019

Credit Assignment Techniques in Stochastic Computation Graphs

Stochastic computation graphs (SCGs) provide a formalism to represent st...

0 Theophane Weber, et al. ∙

research

∙ 11/27/2018

Single-Agent Policy Tree Search With Guarantees

We introduce two novel tree search algorithms that use a policy to guide...

0 Laurent Orseau, et al. ∙

research

∙ 11/15/2018

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

Learning policies on data synthesized by models can in principle quench ...

0 Lars Buesing, et al. ∙

research

∙ 06/05/2018

Relational recurrent neural networks

Memory-based neural networks model temporal data by leveraging an abilit...

2 Adam Santoro, et al. ∙

research

∙ 02/13/2018

Learning to Search with MCTSnets

Planning problems are among the most important and well-studied problems...

0 Arthur Guez, et al. ∙

research

∙ 02/08/2018

Learning and Querying Fast Generative Models for Reinforcement Learning

A key challenge in model-based reinforcement learning (RL) is to synthes...

0 Lars Buesing, et al. ∙

research

∙ 07/19/2017

Imagination-Augmented Agents for Deep Reinforcement Learning

We introduce Imagination-Augmented Agents (I2As), a novel architecture f...

0 Theophane Weber, et al. ∙

research

∙ 07/19/2017

Learning model-based planning from scratch

Conventional wisdom holds that model-based planning is a powerful approa...

0 Razvan Pascanu, et al. ∙

research

∙ 06/05/2017

Visual Interaction Networks

From just a glance, humans can make rich predictions about the future st...

0 Nicholas Watters, et al. ∙

research

∙ 03/28/2016

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

We present a framework for efficient inference in structured image model...

0 S. M. Ali Eslami, et al. ∙

research

∙ 12/24/2015

Deep Reinforcement Learning in Large Discrete Action Spaces

Being able to reason in an environment with a large number of discrete a...

0 Gabriel Dulac-Arnold, et al. ∙

research

∙ 01/07/2013

Automated Variational Inference in Probabilistic Programming

We present a new algorithm for approximate inference in probabilistic pr...

0 David Wingate, et al. ∙

Theophane Weber

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