Arthur Guez | DeepAI

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∙ 06/10/2022

Large-Scale Retrieval for Reinforcement Learning

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

0 Peter C. Humphreys, et al. ∙

∙ 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. ∙

∙ 02/17/2022

Retrieval-Augmented Reinforcement Learning

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

0 Anirudh Goyal, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 02/19/2020

Value-driven Hindsight Modelling

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

17 Arthur Guez, et al. ∙

∙ 11/19/2019

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Constructing agents with planning capabilities has long been one of the ...

23 Julian Schrittwieser, et al. ∙

∙ 10/01/2019

Augmenting learning using symmetry in a biologically-inspired domain

Invariances to translation, rotation and other spatial transformations a...

0 Shruti Mishra, et al. ∙

∙ 01/11/2019

An investigation of model-free planning

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

10 Arthur Guez, et al. ∙

∙ 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. ∙

∙ 02/13/2018

Learning to Search with MCTSnets

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

0 Arthur Guez, et al. ∙

∙ 12/05/2017

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

The game of chess is the most widely-studied domain in the history of ar...

0 David Silver, et al. ∙

∙ 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. ∙

∙ 12/28/2016

The Predictron: End-To-End Learning and Planning

One of the key challenges of artificial intelligence is to learn models ...

0 David Silver, et al. ∙

∙ 02/24/2016

Learning values across many orders of magnitude

Most learning algorithms are not invariant to the scale of the function ...

0 Hado van Hasselt, et al. ∙

∙ 12/15/2015

Increasing the Action Gap: New Operators for Reinforcement Learning

This paper introduces new optimality-preserving operators on Q-functions...

0 Marc G. Bellemare, et al. ∙

∙ 02/09/2014

Better Optimism By Bayes: Adaptive Planning with Rich Models

The computational costs of inference and planning have confined Bayesian...

0 Arthur Guez, et al. ∙

∙ 05/14/2012

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

Bayesian model-based reinforcement learning is a formally elegant approa...

0 Arthur Guez, et al. ∙

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