Multi-Objective Quality-Diversity for Crystal Structure Prediction

1 code implementation • 25 Mar 2024 • Hannah Janmohamed, Marta Wolinska, Shikha Surana, Thomas Pierrot, Aron Walsh, Antoine Cully

This approach overlooks other potentially interesting materials that lie in neighbouring local minima and have different material properties such as conductivity or resistance to deformation.

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX

1 code implementation • 16 Jun 2023 • Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Sasha Abramowitz, Paul Duckworth, Vincent Coyette, Laurence I. Midgley, Elshadai Tegegn, Tristan Kalloniatis, Omayma Mahjoub, Matthew Macfarlane, Andries P. Smit, Nathan Grinsztajn, Raphael Boige, Cemlyn N. Waters, Mohamed A. Mimouni, Ulrich A. Mbou Sob, Ruan de Kock, Siddarth Singh, Daniel Furelos-Blanco, Victor Le, Arnu Pretorius, Alexandre Laterre

Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.

Decision Making reinforcement-learning +1

Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors

no code implementations • 10 Oct 2022 • Shikha Surana, Bryan Lim, Antoine Cully

Data-driven learning based methods have recently been particularly successful at learning robust locomotion controllers for a variety of unstructured terrains.

Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization

1 code implementation • NeurIPS 2023 • Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana, Clément Bonnet, Thomas D. Barrett

Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances.

Combinatorial Optimization Job Shop Scheduling +2