Search Results for author: Julien Perolat

Found 26 papers, 13 papers with code

Fast computation of Nash Equilibria in Imperfect Information Games

no code implementations ICML 2020 Remi Munos, Julien Perolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls

We introduce and analyze a class of algorithms, called Mirror Ascent against an Improved Opponent (MAIO), for computing Nash equilibria in two-player zero-sum games, both in normal form and in sequential imperfect information form.

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

1 code implementation2 Mar 2023 Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas Anthony, Julien Perolat

Progress in fields of machine learning and adversarial planning has benefited significantly from benchmark domains, from checkers and the classic UCI data sets to Go and Diplomacy.

Decision Making Language Modelling

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

no code implementations22 Sep 2022 Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.

reinforcement-learning Reinforcement Learning (RL)

Learning Correlated Equilibria in Mean-Field Games

no code implementations22 Aug 2022 Paul Muller, Romuald Elie, Mark Rowland, Mathieu Lauriere, Julien Perolat, Sarah Perrin, Matthieu Geist, Georgios Piliouras, Olivier Pietquin, Karl Tuyls

The designs of many large-scale systems today, from traffic routing environments to smart grids, rely on game-theoretic equilibrium concepts.

Scaling up Mean Field Games with Online Mirror Descent

1 code implementation28 Feb 2021 Julien Perolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin

We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online Mirror Descent (OMD).

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

1 code implementation NeurIPS 2020 Sarah Perrin, Julien Perolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin

In this paper, we deepen the analysis of continuous time Fictitious Play learning algorithm to the consideration of various finite state Mean Field Game settings (finite horizon, $\gamma$-discounted), allowing in particular for the introduction of an additional common noise.

Navigating the Landscape of Multiplayer Games

no code implementations4 May 2020 Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki, Francisco C. Santos, Mark Rowland, Jerome Connor, Daniel Hennes, Paul Muller, Julien Perolat, Bart De Vylder, Audrunas Gruslys, Remi Munos

Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence.

Multiagent Reinforcement Learning in Games with an Iterated Dominance Solution

no code implementations25 Sep 2019 Yoram Bachrach, Tor Lattimore, Marta Garnelo, Julien Perolat, David Balduzzi, Thomas Anthony, Satinder Singh, Thore Graepel

We show that MARL converges to the desired outcome if the rewards are designed so that exerting effort is the iterated dominance solution, but fails if it is merely a Nash equilibrium.

reinforcement-learning Reinforcement Learning (RL)

Multiagent Evaluation under Incomplete Information

1 code implementation NeurIPS 2019 Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Perolat, Michal Valko, Georgios Piliouras, Remi Munos

This paper investigates the evaluation of learned multiagent strategies in the incomplete information setting, which plays a critical role in ranking and training of agents.

Foolproof Cooperative Learning

no code implementations24 Jun 2019 Alexis Jacq, Julien Perolat, Matthieu Geist, Olivier Pietquin

We prove that in repeated symmetric games, this algorithm is a learning equilibrium.

α-Rank: Multi-Agent Evaluation by Evolution

1 code implementation4 Mar 2019 Shayegan Omidshafiei, Christos Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Perolat, Remi Munos

We introduce {\alpha}-Rank, a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs).

Mathematical Proofs

Open-ended Learning in Symmetric Zero-sum Games

no code implementations23 Jan 2019 David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel

Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'.

Malthusian Reinforcement Learning

no code implementations17 Dec 2018 Joel Z. Leibo, Julien Perolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel

Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation.

Multi-agent Reinforcement Learning reinforcement-learning +1

Re-evaluating Evaluation

2 code implementations NeurIPS 2018 David Balduzzi, Karl Tuyls, Julien Perolat, Thore Graepel

Progress in machine learning is measured by careful evaluation on problems of outstanding common interest.

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

1 code implementation NeurIPS 2017 Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel

To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL).

reinforcement-learning Reinforcement Learning (RL)

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