Matthieu Geist

research

∙ 07/25/2023

Offline Reinforcement Learning with On-Policy Q-Function Regularization

The core challenge of offline reinforcement learning (RL) is dealing wit...

0 Laixi Shi, et al. ∙

research

∙ 07/24/2023

A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning

As with any machine learning problem with limited data, effective offlin...

0 Benjamin Eysenbach, et al. ∙

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

∙ 06/23/2023

GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models

Knowledge distillation is commonly used for compressing neural networks ...

1 Rishabh Agarwal, 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

∙ 05/26/2023

The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model

This paper investigates model robustness in reinforcement learning (RL) ...

5 Laixi Shi, 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

∙ 03/12/2023

Twice Regularized Markov Decision Processes: The Equivalence between Robustness and Regularization

Robust Markov decision processes (MDPs) aim to handle changing or partia...

0 Esther Derman, et al. ∙

research

∙ 02/10/2023

Towards Minimax Optimality of Model-based Robust Reinforcement Learning

We study the sample complexity of obtaining an ϵ-optimal policy in Robus...

0 Pierre Clavier, et al. ∙

research

∙ 01/31/2023

Policy Gradient for s-Rectangular Robust Markov Decision Processes

We present a novel robust policy gradient method (RPG) for s-rectangular...

0 Navdeep Kumar, et al. ∙

research

∙ 01/05/2023

Extreme Q-Learning: MaxEnt RL without Entropy

Modern Deep Reinforcement Learning (RL) algorithms require estimates of ...

0 Divyansh Garg, et al. ∙

research

∙ 12/29/2022

Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games

Mean-field games have been used as a theoretical tool to obtain an appro...

0 Batuhan Yardim, 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

∙ 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

∙ 05/19/2022

Learning Energy Networks with Generalized Fenchel-Young Losses

Energy-based models, a.k.a. energy networks, perform inference by optimi...

0 Mathieu Blondel, 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

∙ 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

∙ 10/12/2021

Twice regularized MDPs and the equivalence between robustness and regularization

Robust Markov decision processes (MDPs) aim to handle changing or partia...

0 Esther Derman, et al. ∙

research

∙ 10/04/2021

Large Batch Experience Replay

Several algorithms have been proposed to sample non-uniformly the replay...

0 Thibault Lahire, 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

∙ 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

∙ 08/12/2021

A functional mirror ascent view of policy gradient methods with function approximation

We use functional mirror ascent to propose a general framework (referred...

13 Sharan Vaswani, 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

∙ 05/17/2021

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

∙ 02/20/2021

How To Train Your HERON

In this paper we apply Deep Reinforcement Learning (Deep RL) and Domain ...

0 Antoine Richard, 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

∙ 07/28/2020

Munchausen Reinforcement Learning

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

0 Nino Vieillard, 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

∙ 06/06/2020

Stable and Efficient Policy Evaluation

Policy evaluation algorithms are essential to reinforcement learning due...

12 Daoming Lyu, 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

∙ 10/28/2019

Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description

Most of the research effort on image-based place recognition is designed...

14 Assia Benbihi, 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

∙ 09/04/2019

Learning Sensor Placement from Demonstration for UAV networks

This work demonstrates how to leverage previous network expert demonstra...

0 Assia Benbihi, 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

∙ 07/07/2019

ELF: Embedded Localisation of Features in pre-trained CNN

This paper introduces a novel feature detector based only on information...

8 Assia Benbihi, 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. ∙

Matthieu Geist

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