570 citations · 2.6k across the 75 of their papers we have counts for
17 papers · 1 filter
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
Sabela Ramos, Sertan Girgin, Léonard Hussenot +9
We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (…
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO
Paul Muller, Mark Rowland, Romuald Elie +6
Recent advances in multiagent learning have seen the introduction ofa family of algorithms that revolve around the population-based trainingmethod PSRO, showing convergence to Nash…
Solving N-player dynamic routing games with congestion: a mean field approach
Theophile Cabannes, Mathieu Lauriere, Julien Perolat +7
The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the funda…
Continuous Control with Action Quantization from Demonstrations
Robert Dadashi, Léonard Hussenot, Damien Vincent +4
In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The proposed…
Generalization in Mean Field Games by Learning Master Policies
Sarah Perrin, Mathieu Laurière, Julien Pérolat +3
Mean Field Games (MFGs) can potentially scale multi-agent systems to extremely large populations of agents. Yet, most of the literature assumes a single initial distribution for th…
Learning Natural Language Generation from Scratch
Alice Martin Donati, Guillaume Quispe, Charles Ollion +3
This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original ap-proach to train conditional language models from scratch by only using reinforcement le…
Implicitly Regularized RL with Implicit Q-Values
Nino Vieillard, Marcin Andrychowicz, Anton Raichuk +2
The -function is a central quantity in many Reinforcement Learning (RL) algorithms for which RL agents behave following a (soft)-greedy policy w.r.t. to . It is a powerful to…
Offline Reinforcement Learning as Anti-Exploration
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard +4
Offline Reinforcement Learning (RL) aims at learning an optimal control from a fixed dataset, without interactions with the system. An agent in this setting should avoid selecting…
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
Nathan Grinsztajn, Johan Ferret, Olivier Pietquin +2
We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL). From theoretical considerations, we show…
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint
Matthieu Geist, Julien Pérolat, Mathieu Laurière +5
Concave Utility Reinforcement Learning (CURL) extends RL from linear to concave utilities in the occupancy measure induced by the agent's policy. This encompasses not only RL but a…
What Matters for Adversarial Imitation Learning?
Manu Orsini, Anton Raichuk, Léonard Hussenot +7
Adversarial imitation learning has become a popular framework for imitation in continuous control. Over the years, several variations of its components were proposed to enhance the…
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
Mathieu Seurin, Florian Strub, Philippe Preux +1
Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize. Intrinsic motivation guidances have thus been developed toward alle…
Hyperparameter Selection for Imitation Learning
Leonard Hussenot, Marcin Andrychowicz, Damien Vincent +11
We address the issue of tuning hyperparameters (HPs) for imitation learning algorithms in the context of continuous-control, when the underlying reward function of the demonstratin…
Mean Field Games Flock! The Reinforcement Learning Way
Sarah Perrin, Mathieu Laurière, Julien Pérolat +3
We present a method enabling a large number of agents to learn how to flock, which is a natural behavior observed in large populations of animals. This problem has drawn a lot of i…
Offline Reinforcement Learning with Pseudometric Learning
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard +3
Offline Reinforcement Learning methods seek to learn a policy from logged transitions of an environment, without any interaction. In the presence of function approximation, and und…
Scaling up Mean Field Games with Online Mirror Descent
Julien Perolat, Sarah Perrin, Romuald Elie +5
We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash equilibriu…
Adversarially Guided Actor-Critic
Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin +2
Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tas…