Search Results for author: Sertan Girgin

Found 11 papers, 6 papers with code

vec2text with Round-Trip Translations

no code implementations14 Sep 2022 Geoffrey Cideron, Sertan Girgin, Anton Raichuk, Olivier Pietquin, Olivier Bachem, Léonard Hussenot

We propose a simple data augmentation technique based on round-trip translations and show in extensive experiments that the resulting vec2text model surprisingly leads to vector spaces that fulfill our four desired properties and that this model strongly outperforms both standard and denoising auto-encoders.

Data Augmentation Denoising +1

Scalable Deep Reinforcement Learning Algorithms for Mean Field Games

no code implementations22 Mar 2022 Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Élie, Olivier Pietquin, Matthieu Geist

One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values.


RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

1 code implementation4 Nov 2021 Sabela Ramos, Sertan Girgin, Léonard Hussenot, Damien Vincent, Hanna Yakubovich, Daniel Toyama, Anita Gergely, Piotr Stanczyk, Raphael Marinier, Jeremiah Harmsen, Olivier Pietquin, Nikola Momchev

We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from Demonstrations, Offline RL or Imitation Learning.

Imitation Learning Offline RL +1

Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation

1 code implementation24 Jun 2021 C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem

We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX.

OpenAI Gym reinforcement-learning

What Matters for Adversarial Imitation Learning?

no code implementations NeurIPS 2021 Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz

To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.

Continuous Control Imitation Learning

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