b'Thore Graepel'

research

∙ 02/15/2022

NeuPL: Neural Population Learning

Learning in strategy games (e.g. StarCraft, poker) requires the discover...

Siqi Liu, et al. ∙

research

∙ 01/05/2022

Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria

A key challenge in the study of multiagent cooperation is the need for i...

Kavya Kopparapu, et al. ∙

research

∙ 09/28/2021

A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works!

Abstract We present PAC-Bayesian bounds for the generalisation error of ...

Thore Graepel, et al. ∙

research

∙ 07/14/2021

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Existing evaluation suites for multi-agent reinforcement learning (MARL)...

Joel Z. Leibo, et al. ∙

research

∙ 06/17/2021

Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers

Two-player, constant-sum games are well studied in the literature, but t...

Luke Marris, et al. ∙

research

∙ 05/25/2021

From Motor Control to Team Play in Simulated Humanoid Football

Intelligent behaviour in the physical world exhibits structure at multip...

Siqi Liu, et al. ∙

research

∙ 03/08/2021

Deep reinforcement learning models the emergent dynamics of human cooperation

Collective action demands that individuals efficiently coordinate how mu...

Kevin R. McKee, et al. ∙

research

∙ 02/08/2021

EigenGame Unloaded: When playing games is better than optimizing

We build on the recently proposed EigenGame that views eigendecompositio...

Ian Gemp, et al. ∙

research

∙ 12/15/2020

Open Problems in Cooperative AI

Problems of cooperation–in which agents seek ways to jointly improve the...

Allan Dafoe, et al. ∙

research

∙ 11/18/2020

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 ...

Karl Tuyls, et al. ∙

research

∙ 10/20/2020

Negotiating Team Formation Using Deep Reinforcement Learning

When autonomous agents interact in the same environment, they must often...

Yoram Bachrach, et al. ∙

research

∙ 10/18/2020

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

Game theoretic views of convention generally rest on notions of common k...

Raphael Koster, et al. ∙

research

∙ 10/01/2020

EigenGame: PCA as a Nash Equilibrium

We present a novel view on principal component analysis (PCA) as a compe...

Ian Gemp, et al. ∙

research

∙ 06/08/2020

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

Recent advances in deep reinforcement learning (RL) have led to consider...

Thomas Anthony, et al. ∙

research

∙ 01/14/2020

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

With the success of modern machine learning, it is becoming increasingly...

David Balduzzi, et al. ∙

research

∙ 12/11/2019

Biases for Emergent Communication in Multi-agent Reinforcement Learning

We study the problem of emergent communication, in which language arises...

Tom Eccles, et al. ∙

research

∙ 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 ...

Julian Schrittwieser, et al. ∙

research

∙ 09/27/2019

A Generalized Training Approach for Multiagent Learning

This paper investigates a population-based training regime based on game...

Paul Müller, et al. ∙

research

∙ 07/11/2019

A Neural Architecture for Designing Truthful and Efficient Auctions

Auctions are protocols to allocate goods to buyers who have preferences ...

Andrea Tacchetti, et al. ∙

research

∙ 05/13/2019

Differentiable Game Mechanics

Deep learning is built on the foundational guarantee that gradient desce...

Alistair Letcher, et al. ∙

research

∙ 03/02/2019

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...

Joel Z. Leibo, et al. ∙

research

∙ 02/19/2019

Emergent Coordination Through Competition

We study the emergence of cooperative behaviors in reinforcement learnin...

Siqi Liu, et al. ∙

research

∙ 01/23/2019

Open-ended Learning in Symmetric Zero-sum Games

Zero-sum games such as chess and poker are, abstractly, functions that e...

David Balduzzi, et al. ∙

research

∙ 12/17/2018

Malthusian Reinforcement Learning

Here we explore a new algorithmic framework for multi-agent reinforcemen...

Joel Z. Leibo, et al. ∙

research

∙ 09/28/2018

Relational Forward Models for Multi-Agent Learning

The behavioral dynamics of multi-agent systems have a rich and orderly s...

Andrea Tacchetti, et al. ∙

research

∙ 07/03/2018

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

Recent progress in artificial intelligence through reinforcement learnin...

Max Jaderberg, et al. ∙

research

∙ 06/11/2018

Adaptive Mechanism Design: Learning to Promote Cooperation

In the future, artificial learning agents are likely to become increasin...

Tobias Baumann, et al. ∙

research

∙ 06/07/2018

Re-evaluating evaluation

Progress in machine learning is measured by careful evaluation on proble...

David Balduzzi, et al. ∙

research

∙ 03/23/2018

Inequity aversion improves cooperation in intertemporal social dilemmas

Groups of humans are often able to find ways to cooperate with one anoth...

Edward Hughes, et al. ∙

research

∙ 03/23/2018

Inequity aversion resolves intertemporal social dilemmas

Groups of humans are often able to find ways to cooperate with one anoth...

Edward Hughes, et al. ∙

research

∙ 03/16/2018

A Generalised Method for Empirical Game Theoretic Analysis

This paper provides theoretical bounds for empirical game theoretical an...

Karl Tuyls, et al. ∙

research

∙ 02/15/2018

The Mechanics of n-Player Differentiable Games

The cornerstone underpinning deep learning is the guarantee that gradien...

David Balduzzi, et al. ∙

research

∙ 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...

David Silver, et al. ∙

research

∙ 11/14/2017

Symmetric Decomposition of Asymmetric Games

We introduce new theoretical insights into two-population asymmetric gam...

Karl Tuyls, et al. ∙

research

∙ 11/02/2017

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

To achieve general intelligence, agents must learn how to interact with ...

Marc Lanctot, et al. ∙

research

∙ 07/20/2017

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

Humanity faces numerous problems of common-pool resource appropriation. ...

Julien Perolat, et al. ∙

research

∙ 06/16/2017

Value-Decomposition Networks For Cooperative Multi-Agent Learning

We study the problem of cooperative multi-agent reinforcement learning w...

Peter Sunehag, et al. ∙

research

∙ 02/10/2017

Multi-agent Reinforcement Learning in Sequential Social Dilemmas

Matrix games like Prisoner's Dilemma have guided research on social dile...

Joel Z. Leibo, et al. ∙

research

∙ 03/07/2016

Learning Shared Representations in Multi-task Reinforcement Learning

We investigate a paradigm in multi-task reinforcement learning (MT-RL) i...

Diana Borsa, et al. ∙

research

∙ 06/09/2015

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...

Diana Borsa, et al. ∙

research

∙ 10/31/2014

A Comparison of learning algorithms on the Arcade Learning Environment

Reinforcement learning agents have traditionally been evaluated on small...

Aaron Defazio, et al. ∙

research

∙ 12/05/2012

Compiling Relational Database Schemata into Probabilistic Graphical Models

Instead of requiring a domain expert to specify the probabilistic depend...

Sameer Singh, et al. ∙

research

∙ 07/19/2012

SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases

The Internet has enabled the creation of a growing number of large-scale...

Simon Lacoste-Julien, et al. ∙

research

∙ 06/27/2012

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...

Yoram Bachrach, et al. ∙

research

∙ 10/21/2011

Kernel Topic Models

Latent Dirichlet Allocation models discrete data as a mixture of discret...

Philipp Hennig, et al. ∙

Thore Graepel

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