Thore Graepel

is this you? claim profile

∙ 0 followers
Google ∙ Microsoft

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

Open Problems in Cooperative AI

Problems of cooperation–in which agents seek ways to jointly improve the...
12/15/2020 ∙ by Allan Dafoe, et al. ∙ 0 ∙ share

read it
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 ...
11/18/2020 ∙ by Karl Tuyls, et al. ∙ 11 ∙ share

read it
Negotiating Team Formation Using Deep Reinforcement Learning

When autonomous agents interact in the same environment, they must often...
10/20/2020 ∙ by Yoram Bachrach, et al. ∙ 0 ∙ share

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
Differentiable Game Mechanics

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

read it
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

read it
Emergent Coordination Through Competition

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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
Re-evaluating evaluation

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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
Kernel Topic Models

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

read it

Thore Graepel

Featured Co-authors