Thore Graepel

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Google ∙ Microsoft

Professor of Machine Learning, Computer Science at UCL, Research Lead at Google DeepMind.

Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences

Game theoretic views of convention generally rest on notions of common k...
10/18/2020 ∙ by Raphael Koster, et al. ∙ 0 ∙ share

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EigenGame: PCA as a Nash Equilibrium

We present a novel view on principal component analysis (PCA) as a compe...
10/01/2020 ∙ by Ian Gemp, et al. ∙ 6 ∙ share

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Learning to Play No-Press Diplomacy with Best Response Policy Iteration

Recent advances in deep reinforcement learning (RL) have led to consider...
06/08/2020 ∙ by Thomas Anthony, et al. ∙ 0 ∙ share

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Smooth markets: A basic mechanism for organizing gradient-based learners

With the success of modern machine learning, it is becoming increasingly...
01/14/2020 ∙ by David Balduzzi, et al. ∙ 10 ∙ share

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Biases for Emergent Communication in Multi-agent Reinforcement Learning

We study the problem of emergent communication, in which language arises...
12/11/2019 ∙ by Tom Eccles, et al. ∙ 0 ∙ share

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

Constructing agents with planning capabilities has long been one of the ...
11/19/2019 ∙ by Julian Schrittwieser, et al. ∙ 23 ∙ 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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A Neural Architecture for Designing Truthful and Efficient Auctions

Auctions are protocols to allocate goods to buyers who have preferences ...
07/11/2019 ∙ by Andrea Tacchetti, et al. ∙ 0 ∙ share

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Differentiable Game Mechanics

Deep learning is built on the foundational guarantee that gradient desce...
05/13/2019 ∙ by Alistair Letcher, et al. ∙ 0 ∙ share

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Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research

Evolution has produced a multi-scale mosaic of interacting adaptive unit...
03/02/2019 ∙ by Joel Z. Leibo, et al. ∙ 16 ∙ 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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Open-ended Learning in Symmetric Zero-sum Games

Zero-sum games such as chess and poker are, abstractly, functions that e...
01/23/2019 ∙ by David Balduzzi, et al. ∙ 16 ∙ share

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Malthusian Reinforcement Learning

Here we explore a new algorithmic framework for multi-agent reinforcemen...
12/17/2018 ∙ by Joel Z. Leibo, et al. ∙ 10 ∙ share

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Relational Forward Models for Multi-Agent Learning

The behavioral dynamics of multi-agent systems have a rich and orderly s...
09/28/2018 ∙ by Andrea Tacchetti, et al. ∙ 4 ∙ share

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Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

Recent progress in artificial intelligence through reinforcement learnin...
07/03/2018 ∙ by Max Jaderberg, et al. ∙ 2 ∙ share

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Adaptive Mechanism Design: Learning to Promote Cooperation

In the future, artificial learning agents are likely to become increasin...
06/11/2018 ∙ by Tobias Baumann, et al. ∙ 0 ∙ share

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Re-evaluating evaluation

Progress in machine learning is measured by careful evaluation on proble...
06/07/2018 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Inequity aversion improves cooperation in intertemporal social dilemmas

Groups of humans are often able to find ways to cooperate with one anoth...
03/23/2018 ∙ by Edward Hughes, et al. ∙ 0 ∙ share

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Inequity aversion resolves intertemporal social dilemmas

Groups of humans are often able to find ways to cooperate with one anoth...
03/23/2018 ∙ by Edward Hughes, et al. ∙ 0 ∙ share

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A Generalised Method for Empirical Game Theoretic Analysis

This paper provides theoretical bounds for empirical game theoretical an...
03/16/2018 ∙ by Karl Tuyls, et al. ∙ 0 ∙ share

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The Mechanics of n-Player Differentiable Games

The cornerstone underpinning deep learning is the guarantee that gradien...
02/15/2018 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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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...
12/05/2017 ∙ by David Silver, et al. ∙ 0 ∙ share

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Symmetric Decomposition of Asymmetric Games

We introduce new theoretical insights into two-population asymmetric gam...
11/14/2017 ∙ by Karl Tuyls, et al. ∙ 0 ∙ share

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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

To achieve general intelligence, agents must learn how to interact with ...
11/02/2017 ∙ by Marc Lanctot, et al. ∙ 0 ∙ share

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A multi-agent reinforcement learning model of common-pool resource appropriation

Humanity faces numerous problems of common-pool resource appropriation. ...
07/20/2017 ∙ by Julien Perolat, et al. ∙ 0 ∙ share

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Value-Decomposition Networks For Cooperative Multi-Agent Learning

We study the problem of cooperative multi-agent reinforcement learning w...
06/16/2017 ∙ by Peter Sunehag, et al. ∙ 0 ∙ share

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Multi-agent Reinforcement Learning in Sequential Social Dilemmas

Matrix games like Prisoner's Dilemma have guided research on social dile...
02/10/2017 ∙ by Joel Z. Leibo, et al. ∙ 0 ∙ share

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Learning Shared Representations in Multi-task Reinforcement Learning

We investigate a paradigm in multi-task reinforcement learning (MT-RL) i...
03/07/2016 ∙ by Diana Borsa, et al. ∙ 0 ∙ share

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The Wreath Process: A totally generative model of geometric shape based on nested symmetries

We consider the problem of modelling noisy but highly symmetric shapes t...
06/09/2015 ∙ by Diana Borsa, et al. ∙ 0 ∙ share

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A Comparison of learning algorithms on the Arcade Learning Environment

Reinforcement learning agents have traditionally been evaluated on small...
10/31/2014 ∙ by Aaron Defazio, et al. ∙ 0 ∙ share

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Compiling Relational Database Schemata into Probabilistic Graphical Models

Instead of requiring a domain expert to specify the probabilistic depend...
12/05/2012 ∙ by Sameer Singh, et al. ∙ 0 ∙ share

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SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases

The Internet has enabled the creation of a growing number of large-scale...
07/19/2012 ∙ by Simon Lacoste-Julien, et al. ∙ 0 ∙ share

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How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing

We propose a new probabilistic graphical model that jointly models the d...
06/27/2012 ∙ by Yoram Bachrach, et al. ∙ 0 ∙ share

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Kernel Topic Models

Latent Dirichlet Allocation models discrete data as a mixture of discret...
10/21/2011 ∙ by Philipp Hennig, et al. ∙ 0 ∙ share

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Thore Graepel

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