On the importance of data collection for training general goal-reaching policies

no code implementations • 7 Nov 2022 • Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem

Are the quantity and quality of data truly transformative to the performance of a general controller?

Continuous Control

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

no code implementations • 16 Mar 2022 • Alexis Jacq, Johan Ferret, Olivier Pietquin, Matthieu Geist

We deem those states and corresponding actions important since they explain the difference in performance between the default and the new, lazy policy.

Atari Games Decision Making +2

Acme: A Research Framework for Distributed Reinforcement Learning

5 code implementations • 1 Jun 2020 • Matthew W. Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Nikola Momchev, Danila Sinopalnikov, Piotr Stańczyk, Sabela Ramos, Anton Raichuk, Damien Vincent, Léonard Hussenot, Robert Dadashi, Gabriel Dulac-Arnold, Manu Orsini, Alexis Jacq, Johan Ferret, Nino Vieillard, Seyed Kamyar Seyed Ghasemipour, Sertan Girgin, Olivier Pietquin, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Abe Friesen, Ruba Haroun, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.

DQN Replay Dataset reinforcement-learning +1

Foolproof Cooperative Learning

no code implementations • 24 Jun 2019 • Alexis Jacq, Julien Perolat, Matthieu Geist, Olivier Pietquin

We prove that in repeated symmetric games, this algorithm is a learning equilibrium.