Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization. - Alexandre Laterre; Yunguan Fu; Mohamed Khalil Jabri; Alain-Sam Cohen; David Kas; Karl Hajjar; Torbjorn S. Dahl; Amine Kerkeni; Karim Beguir (CoRR, 2018)

Learning Compositional Neural Programs with Recursive Tree Search and Planning. - Thomas Pierrot; Guillaume Ligner; Scott E. Reed; Olivier Sigaud; Nicolas Perrin 001; Alexandre Laterre; David Kas; Karim Beguir; Nando de Freitas (NeurIPS, 2019)

A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning. - Arnu Pretorius; Scott Alexander Cameron; Elan Van Biljon; Tom Makkink; Shahil Mawjee; Jeremy du Plessis; Jonathan P. Shock; Alexandre Laterre; Karim Beguir (NeurIPS, 2020)

Learning Compositional Neural Programs for Continuous Control. - Thomas Pierrot; Nicolas Perrin 001; Feryal M. P. Behbahani; Alexandre Laterre; Olivier Sigaud; Karim Beguir; Nando de Freitas (CoRR, 2020)

Offline Reinforcement Learning Hands-On. - Louis Monier; Jakub Kmec; Alexandre Laterre; Thomas Pierrot; Valentin Courgeau; Olivier Sigaud; Karim Beguir (CoRR, 2020)

Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning. - Marcin J. Skwark; Nicolás López Carranza; Thomas Pierrot; Joe Phillips; Slim Said; Alexandre Laterre; Amine Kerkeni; Ugur Sahin; Karim Beguir (CoRR, 2020)

Mava: a research framework for distributed multi-agent reinforcement learning. - Arnu Pretorius; Kale-ab Abebe Tessera; Andries P. Smit; Claude Formanek; St John Grimbly; Kevin Eloff; Siphelele Danisa; Lawrence Francis; Jonathan P. Shock; Herman Kamper; Willie Brink; Herman A. Engelbrecht; et al. (CoRR, 2021)

One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning. - Clément Bonnet; Paul Caron; Thomas D. Barrett; Ian Davies; Alexandre Laterre (CoRR, 2021)

Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration. - Thomas D. Barrett; Christopher W. F. Parsonson; Alexandre Laterre (CoRR, 2022)

Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function. - Clément Bonnet; Laurence I. Midgley; Alexandre Laterre (CoRR, 2022)

Reinforcement Learning for Branch-and-Bound Optimisation Using Retrospective Trajectories. - Christopher W. F. Parsonson; Alexandre Laterre; Thomas D. Barrett (AAAI, 2023)

Combinatorial Optimization with Policy Adaptation using Latent Space Search. - Félix Chalumeau; Shikha Surana; Clément Bonnet; Nathan Grinsztajn; Arnu Pretorius; Alexandre Laterre; Tom Barrett (NeurIPS, 2023)

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. - Clément Bonnet; Daniel Luo; Donal Byrne; Shikha Surana; Vincent Coyette; Paul Duckworth; Laurence I. Midgley; Tristan Kalloniatis; Sasha Abramowitz; Cemlyn N. Waters; Andries P. Smit; Nathan Grinsztajn; et al. (CoRR, 2023)

Combinatorial Optimization with Policy Adaptation using Latent Space Search. - Félix Chalumeau; Shikha Surana; Clément Bonnet; Nathan Grinsztajn; Arnu Pretorius; Alexandre Laterre; Thomas D. Barrett (CoRR, 2023)

Early computational detection of potential high-risk SARS-CoV-2 variants. - Karim Beguir; Marcin J. Skwark; Yunguan Fu; Thomas Pierrot; Nicolás López Carranza; Alexandre Laterre; Ibtissem Kadri; Abir Korched; Anna U. Lowegard; Bonny Gaby Lui; Bianca Sänger; Yunpeng Liu; et al. (Comput. Biol. Medicine, 2023)

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. - Clément Bonnet; Daniel Luo; Donal Byrne; Shikha Surana; Sasha Abramowitz; Paul Duckworth; Vincent Coyette; Laurence Illing Midgley; Elshadai Tegegn; Tristan Kalloniatis; Omayma Mahjoub; Matthew Macfarlane; et al. (ICLR, 2024)

SPO: Sequential Monte Carlo Policy Optimisation. - Matthew Macfarlane; Edan Toledo; Donal Byrne; Paul Duckworth; Alexandre Laterre (NeurIPS, 2024)

SMX: Sequential Monte Carlo Planning for Expert Iteration. - Matthew V. Macfarlane; Edan Toledo; Donal Byrne; Siddarth Singh; Paul Duckworth; Alexandre Laterre (CoRR, 2024)