Search Results for author: Romuald Elie

Found 14 papers, 2 papers with code

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.


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.

An overview of active learning methods for insurance with fairness appreciation

no code implementations17 Dec 2021 Romuald Elie, Caroline Hillairet, François Hu, Marc Juillard

This paper addresses and solves some challenges in the adoption of machine learning in insurance with the democratization of model deployment.

Active Learning Fairness

Evolutionary Dynamics and $Φ$-Regret Minimization in Games

no code implementations28 Jun 2021 Georgios Piliouras, Mark Rowland, Shayegan Omidshafiei, Romuald Elie, Daniel Hennes, Jerome Connor, Karl Tuyls

Importantly, $\Phi$-regret enables learning agents to consider deviations from and to mixed strategies, generalizing several existing notions of regret such as external, internal, and swap regret, and thus broadening the insights gained from regret-based analysis of learning algorithms.

online learning

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).

Conditional Loss and Deep Euler Scheme for Time Series Generation

no code implementations10 Feb 2021 Carl Remlinger, Joseph Mikael, Romuald Elie

We introduce three new generative models for time series that are based on Euler discretization of Stochastic Differential Equations (SDEs) and Wasserstein metrics.

Time Series Transfer Learning

An Euler-based GAN for time series

no code implementations1 Jan 2021 Carl Remlinger, Joseph Mickael, Romuald Elie

A new model of generative adversarial networks for time series based on Euler scheme and Wasserstein distances including Sinkhorn divergence is proposed.

Time Series Transfer Learning

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.

Reinforcement Learning in Economics and Finance

no code implementations22 Mar 2020 Arthur Charpentier, Romuald Elie, Carl Remlinger

As in multi-armed bandit problems, when an agent picks an action, he can not infer ex-post the rewards induced by other action choices.

online learning reinforcement-learning

On the Convergence of Model Free Learning in Mean Field Games

no code implementations4 Jul 2019 Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin

In order to design scalable algorithms for systems with a large population of interacting agents (e. g. swarms), this paper focuses on Mean Field MAS, where the number of agents is asymptotically infinite.

Cannot find the paper you are looking for? You can Submit a new open access paper.