570 citations · 2.6k across the 75 of their papers we have counts for
12 papers · 1 filter
Self-Imitation Advantage Learning
Johan Ferret, Olivier Pietquin, Matthieu Geist
Self-imitation learning is a Reinforcement Learning (RL) method that encourages actions whose returns were higher than expected, which helps in hard exploration and sparse reward p…
Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering
Aaqib Saeed, David Grangier, Olivier Pietquin +1
We propose CHARM, a method for training a single neural network across inconsistent input channels. Our work is motivated by Electroencephalography (EEG), where data collection pro…
Supervised Seeded Iterated Learning for Interactive Language Learning
Yuchen Lu, Soumye Singhal, Florian Strub +2
Language drift has been one of the major obstacles to train language models through interaction. When word-based conversational agents are trained towards completing a task, they t…
A Machine of Few Words -- Interactive Speaker Recognition with Reinforcement Learning
Mathieu Seurin, Florian Strub, Philippe Preux +1
Speaker recognition is a well known and studied task in the speech processing domain. It has many applications, either for security or speaker adaptation of personal devices. In th…
Munchausen Reinforcement Learning
Nino Vieillard, Olivier Pietquin, Matthieu Geist
Bootstrapping is a core mechanism in Reinforcement Learning (RL). Most algorithms, based on temporal differences, replace the true value of a transiting state by their current esti…
The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction
Alice Martin, Charles Ollion, Florian Strub +2
This paper introduces the Sequential Monte Carlo Transformer, an original approach that naturally captures the observations distribution in a transformer architecture. The keys, qu…
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
Sarah Perrin, Julien Perolat, Mathieu Laurière +3
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, $…
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk +9
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple,…
Show me the Way: Intrinsic Motivation from Demonstrations
Léonard Hussenot, Robert Dadashi, Matthieu Geist +1
The study of exploration in the domain of decision making has a long history but remains actively debated. From the vast literature that addressed this topic for decades under vari…
Primal Wasserstein Imitation Learning
Robert Dadashi, Léonard Hussenot, Matthieu Geist +1
Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert. In the present work, we propose a new IL method based on a conceptually simple algori…
Leverage the Average: an Analysis of KL Regularization in RL
Nino Vieillard, Tadashi Kozuno, Bruno Scherrer +3
Recent Reinforcement Learning (RL) algorithms making use of Kullback-Leibler (KL) regularization as a core component have shown outstanding performance. Yet, only little is underst…
Countering Language Drift with Seeded Iterated Learning
Yuchen Lu, Soumye Singhal, Florian Strub +2
Pretraining on human corpus and then finetuning in a simulator has become a standard pipeline for training a goal-oriented dialogue agent. Nevertheless, as soon as the agents are f…