Julien Perolat

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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

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The Advantage Regret-Matching Actor-Critic

Regret minimization has played a key role in online learning, equilibriu...
08/27/2020 ∙ by Audrūnas Gruslys, et al. ∙ 0 ∙ share

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Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications

In this paper, we deepen the analysis of continuous time Fictitious Play...
07/05/2020 ∙ by Sarah Perrin, et al. ∙ 5 ∙ 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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Navigating the Landscape of Multiplayer Games to Probe the Drosophila of AI

Multiplayer games have a long history in being used as key testbeds for ...
05/04/2020 ∙ by Shayegan Omidshafiei, et al. ∙ 11 ∙ share

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From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

In this paper we investigate the Follow the Regularized Leader dynamics ...
02/19/2020 ∙ by Julien Perolat, et al. ∙ 32 ∙ 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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Multiagent Evaluation under Incomplete Information

This paper investigates the evaluation of learned multiagent strategies ...
09/21/2019 ∙ by Mark Rowland, et al. ∙ 24 ∙ share

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OpenSpiel: A Framework for Reinforcement Learning in Games

OpenSpiel is a collection of environments and algorithms for research in...
08/26/2019 ∙ by Marc Lanctot, et al. ∙ 12 ∙ share

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Approximate Fictitious Play for Mean Field Games

The theory of Mean Field Games (MFG) allows characterizing the Nash equi...
07/04/2019 ∙ by Romuald Elie, et al. ∙ 0 ∙ share

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Foolproof Cooperative Learning

This paper extends the notion of equilibrium in game theory to learning ...
06/24/2019 ∙ by Alexis Jacq, et al. ∙ 0 ∙ share

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Neural Replicator Dynamics

In multiagent learning, agents interact in inherently nonstationary envi...
06/01/2019 ∙ by Shayegan Omidshafiei, et al. ∙ 12 ∙ share

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Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent

In this paper, we present exploitability descent, a new algorithm to com...
03/13/2019 ∙ by Edward Lockhart, et al. ∙ 20 ∙ share

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α-Rank: Multi-Agent Evaluation by Evolution

We introduce α-Rank, a principled evolutionary dynamics methodology, for...
03/04/2019 ∙ by Shayegan Omidshafiei, et al. ∙ 0 ∙ 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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Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Optimization of parameterized policies for reinforcement learning (RL) i...
10/21/2018 ∙ by Sriram Srinivasan, et al. ∙ 8 ∙ share

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Playing the Game of Universal Adversarial Perturbations

We study the problem of learning classifiers robust to universal adversa...
09/20/2018 ∙ by Julien Perolat, et al. ∙ 12 ∙ 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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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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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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Julien Perolat

Featured Co-authors

Zhe Wang

Nicolas Heess

Remi Munos

David Silver

Michal Valko

Olivier Pietquin

Satinder Singh

Joel Z. Leibo

Georgios Piliouras

Thore Graepel

Karl Tuyls

Game Plan: What AI can do for Football, and What Football can do for AI

The Advantage Regret-Matching Actor-Critic

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications

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

Navigating the Landscape of Multiplayer Games to Probe the Drosophila of AI

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

A Generalized Training Approach for Multiagent Learning

Multiagent Evaluation under Incomplete Information

OpenSpiel: A Framework for Reinforcement Learning in Games

Approximate Fictitious Play for Mean Field Games

Foolproof Cooperative Learning

Neural Replicator Dynamics

Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent

α-Rank: Multi-Agent Evaluation by Evolution

Open-ended Learning in Symmetric Zero-sum Games

Malthusian Reinforcement Learning

Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Playing the Game of Universal Adversarial Perturbations

Re-evaluating evaluation

A Generalised Method for Empirical Game Theoretic Analysis

Symmetric Decomposition of Asymmetric Games

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

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