Julien Perolat

research

∙ 03/02/2023

Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning

Progress in fields of machine learning and adversarial planning has bene...

0 Marc Lanctot, et al. ∙

research

∙ 09/22/2022

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

The Game Theory Multi-Agent team at DeepMind studies several aspects...

3 Ian Gemp, et al. ∙

research

∙ 08/22/2022

Learning Correlated Equilibria in Mean-Field Games

The designs of many large-scale systems today, from traffic routing envi...

0 Paul Müller, et al. ∙

research

∙ 06/30/2022

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning

We introduce DeepNash, an autonomous agent capable of learning to play t...

6 Julien Perolat, et al. ∙

research

∙ 11/16/2021

Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO

Recent advances in multiagent learning have seen the introduction ofa fa...

0 Paul Müller, et al. ∙

research

∙ 10/22/2021

Solving N-player dynamic routing games with congestion: a mean field approach

The recent emergence of navigational tools has changed traffic patterns ...

0 Theophile Cabannes, et al. ∙

research

∙ 10/20/2021

Shaking the foundations: delusions in sequence models for interaction and control

The recent phenomenal success of language models has reinvigorated machi...

68 Pedro A. Ortega, et al. ∙

research

∙ 09/20/2021

Generalization in Mean Field Games by Learning Master Policies

Mean Field Games (MFGs) can potentially scale multi-agent systems to ext...

0 Sarah Perrin, et al. ∙

research

∙ 06/07/2021

Concave Utility Reinforcement Learning: the Mean-field Game viewpoint

Concave Utility Reinforcement Learning (CURL) extends RL from linear to ...

0 Matthieu Geist, et al. ∙

research

∙ 05/17/2021

Mean Field Games Flock! The Reinforcement Learning Way

We present a method enabling a large number of agents to learn how to fl...

10 Sarah Perrin, et al. ∙

research

∙ 02/28/2021

Scaling up Mean Field Games with Online Mirror Descent

We address scaling up equilibrium computation in Mean Field Games (MFGs)...

0 Julien Perolat, 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 ...

11 Karl Tuyls, et al. ∙

research

∙ 08/27/2020

The Advantage Regret-Matching Actor-Critic

Regret minimization has played a key role in online learning, equilibriu...

0 Audrūnas Gruslys, et al. ∙

research

∙ 07/05/2020

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

In this paper, we deepen the analysis of continuous time Fictitious Play...

5 Sarah Perrin, 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...

0 Thomas Anthony, et al. ∙

research

∙ 05/04/2020

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 ...

11 Shayegan Omidshafiei, et al. ∙

research

∙ 02/19/2020

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

In this paper we investigate the Follow the Regularized Leader dynamics ...

32 Julien Perolat, et al. ∙

research

∙ 09/27/2019

A Generalized Training Approach for Multiagent Learning

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

20 Paul Müller, et al. ∙

research

∙ 09/21/2019

Multiagent Evaluation under Incomplete Information

This paper investigates the evaluation of learned multiagent strategies ...

24 Mark Rowland, et al. ∙

research

∙ 08/26/2019

OpenSpiel: A Framework for Reinforcement Learning in Games

OpenSpiel is a collection of environments and algorithms for research in...

12 Marc Lanctot, et al. ∙

research

∙ 07/04/2019

Approximate Fictitious Play for Mean Field Games

The theory of Mean Field Games (MFG) allows characterizing the Nash equi...

0 Romuald Elie, et al. ∙

research

∙ 06/24/2019

Foolproof Cooperative Learning

This paper extends the notion of equilibrium in game theory to learning ...

0 Alexis Jacq, et al. ∙

research

∙ 06/01/2019

Neural Replicator Dynamics

In multiagent learning, agents interact in inherently nonstationary envi...

12 Shayegan Omidshafiei, et al. ∙

research

∙ 03/13/2019

Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent

In this paper, we present exploitability descent, a new algorithm to com...

20 Edward Lockhart, et al. ∙

research

∙ 03/04/2019

α-Rank: Multi-Agent Evaluation by Evolution

We introduce α-Rank, a principled evolutionary dynamics methodology, for...

0 Shayegan Omidshafiei, 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...

16 David Balduzzi, et al. ∙

research

∙ 12/17/2018

Malthusian Reinforcement Learning

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

10 Joel Z. Leibo, et al. ∙

research

∙ 10/21/2018

Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Optimization of parameterized policies for reinforcement learning (RL) i...

8 Sriram Srinivasan, et al. ∙

research

∙ 09/20/2018

Playing the Game of Universal Adversarial Perturbations

We study the problem of learning classifiers robust to universal adversa...

12 Julien Perolat, et al. ∙

research

∙ 06/07/2018

Re-evaluating evaluation

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

0 David Balduzzi, et al. ∙

research

∙ 03/16/2018

A Generalised Method for Empirical Game Theoretic Analysis

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

0 Karl Tuyls, et al. ∙

research

∙ 11/14/2017

Symmetric Decomposition of Asymmetric Games

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

0 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 ...

0 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. ...

0 Julien Perolat, et al. ∙

Julien Perolat

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