Olivier Pietquin

research

∙ 06/26/2023

On Imitation in Mean-field Games

We explore the problem of imitation learning (IL) in the context of mean...

0 Giorgia Ramponi, et al. ∙

research

∙ 05/31/2023

Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback

Despite the seeming success of contemporary grounded text generation sys...

0 Paul Roit, et al. ∙

research

∙ 05/22/2023

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice

Mirror descent value iteration (MDVI), an abstraction of Kullback-Leible...

0 Toshinori Kitamura, et al. ∙

research

∙ 05/02/2023

Get Back Here: Robust Imitation by Return-to-Distribution Planning

We consider the Imitation Learning (IL) setup where expert data are not ...

0 Geoffrey Cideron, et al. ∙

research

∙ 02/07/2023

Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision

We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that...

0 Eugene Kharitonov, et al. ∙

research

∙ 01/30/2023

SingSong: Generating musical accompaniments from singing

We present SingSong, a system that generates instrumental music to accom...

4 Chris Donahue, et al. ∙

research

∙ 11/07/2022

C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining

Given a particular embodiment, we propose a novel method (C3PO) that lea...

0 Alexis Jacq, et al. ∙

research

∙ 09/30/2022

Emergent Communication: Generalization and Overfitting in Lewis Games

Lewis signaling games are a class of simple communication games for simu...

0 Mathieu Rita, et al. ∙

research

∙ 09/14/2022

vec2text with Round-Trip Translations

We investigate models that can generate arbitrary natural language text ...

0 Geoffrey Cideron, et al. ∙

research

∙ 09/07/2022

AudioLM: a Language Modeling Approach to Audio Generation

We introduce AudioLM, a framework for high-quality audio generation with...

17 Zalán Borsos, 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

∙ 05/27/2022

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal

In this work, we consider and analyze the sample complexity of model-fre...

6 Tadashi Kozuno, et al. ∙

research

∙ 05/25/2022

Learning Mean Field Games: A Survey

Non-cooperative and cooperative games with a very large number of player...

0 Mathieu Laurière, et al. ∙

research

∙ 04/27/2022

On the role of population heterogeneity in emergent communication

Populations have often been perceived as a structuring component for lan...

0 Mathieu Rita, et al. ∙

research

∙ 03/16/2022

Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act

Traditionally, Reinforcement Learning (RL) aims at deciding how to act o...

0 Alexis Jacq, 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

∙ 11/04/2021

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

We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for re...

15 Sabela Ramos, 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

∙ 10/19/2021

Continuous Control with Action Quantization from Demonstrations

In Reinforcement Learning (RL), discrete actions, as opposed to continuo...

0 Robert Dadashi, 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

∙ 09/20/2021

Learning Natural Language Generation from Scratch

This paper introduces TRUncated ReinForcement Learning for Language (Tru...

6 Alice Martin Donati, et al. ∙

research

∙ 08/16/2021

Implicitly Regularized RL with Implicit Q-Values

The Q-function is a central quantity in many Reinforcement Learning (RL)...

0 Nino Vieillard, et al. ∙

research

∙ 06/11/2021

Offline Reinforcement Learning as Anti-Exploration

Offline Reinforcement Learning (RL) aims at learning an optimal control ...

0 Shideh Rezaeifar, et al. ∙

research

∙ 06/08/2021

There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning

We propose to learn to distinguish reversible from irreversible actions ...

0 Nathan Grinsztajn, 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

∙ 06/01/2021

What Matters for Adversarial Imitation Learning?

Adversarial imitation learning has become a popular framework for imitat...

0 Manu Orsini, et al. ∙

research

∙ 05/25/2021

Hyperparameter Selection for Imitation Learning

We address the issue of tuning hyperparameters (HPs) for imitation learn...

7 Léonard Hussenot, et al. ∙

research

∙ 05/20/2021

Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness

Sparse rewards are double-edged training signals in reinforcement learni...

10 Mathieu Seurin, 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

∙ 03/02/2021

Offline Reinforcement Learning with Pseudometric Learning

Offline Reinforcement Learning methods seek to learn a policy from logge...

0 Robert Dadashi, 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

∙ 02/08/2021

Adversarially Guided Actor-Critic

Despite definite success in deep reinforcement learning problems, actor-...

5 Yannis Flet-Berliac, et al. ∙

research

∙ 12/22/2020

Self-Imitation Advantage Learning

Self-imitation learning is a Reinforcement Learning (RL) method that enc...

0 Johan Ferret, et al. ∙

research

∙ 10/06/2020

Supervised Seeded Iterated Learning for Interactive Language Learning

Language drift has been one of the major obstacles to train language mod...

0 Yuchen Lu, et al. ∙

research

∙ 08/07/2020

A Machine of Few Words – Interactive Speaker Recognition with Reinforcement Learning

Speaker recognition is a well known and studied task in the speech proce...

0 Mathieu Seurin, et al. ∙

research

∙ 07/28/2020

Munchausen Reinforcement Learning

Bootstrapping is a core mechanism in Reinforcement Learning (RL). Most a...

0 Nino Vieillard, et al. ∙

research

∙ 07/15/2020

The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction

This paper introduces the Sequential Monte Carlo Transformer, an origina...

65 Alice Martin, 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

∙ 06/23/2020

Show me the Way: Intrinsic Motivation from Demonstrations

The study of exploration in Reinforcement Learning (RL) has a long histo...

0 Léonard Hussenot, et al. ∙

research

∙ 06/10/2020

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

In recent years, on-policy reinforcement learning (RL) has been successf...

0 Marcin Andrychowicz, et al. ∙

research

∙ 06/08/2020

Primal Wasserstein Imitation Learning

Imitation Learning (IL) methods seek to match the behavior of an agent w...

0 Robert Dadashi, et al. ∙

research

∙ 05/29/2020

Reinforcement Learning

Reinforcement learning (RL) is a general framework for adaptive control,...

0 Olivier Buffer, et al. ∙

research

∙ 03/31/2020

Leverage the Average: an Analysis of Regularization in RL

Building upon the formalism of regularized Markov decision processes, we...

7 Nino Vieillard, et al. ∙

research

∙ 03/28/2020

Countering Language Drift with Seeded Iterated Learning

Supervised learning methods excel at capturing statistical properties of...

1 Yuchen Lu, et al. ∙

research

∙ 10/21/2019

Self-Educated Language Agent With Hindsight Experience Replay For Instruction Following

Language creates a compact representation of the world and allows the de...

26 Geoffrey Cideron, et al. ∙

research

∙ 10/21/2019

Momentum in Reinforcement Learning

We adapt the optimization's concept of momentum to reinforcement learnin...

0 Nino Vieillard, et al. ∙

research

∙ 10/18/2019

On Connections between Constrained Optimization and Reinforcement Learning

Dynamic Programming (DP) provides standard algorithms to solve Markov De...

0 Nino Vieillard, et al. ∙

research

∙ 10/04/2019

"I'm sorry Dave, I'm afraid I can't do that" Deep Q-learning from forbidden action

The use of Reinforcement Learning (RL) is still restricted to simulation...

0 Mathieu Seurin, et al. ∙

research

∙ 07/18/2019

Credit Assignment as a Proxy for Transfer in Reinforcement Learning

The ability to transfer representations to novel environments and tasks ...

2 Johan Ferret, 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. ∙

Olivier Pietquin

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