SE(3) Equivariant Augmented Coupling Flows

1 code implementation • NeurIPS 2023 • Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato

Coupling normalizing flows allow for fast sampling and density evaluation, making them the tool of choice for probabilistic modeling of physical systems.

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

Synthesis of separation processes with reinforcement learning

1 code implementation • 3 Nov 2022 • Stephan C. P. A. van Kalmthout, Laurence I. Midgley, Meik B. Franke

This paper shows the implementation of reinforcement learning (RL) in commercial flowsheet simulator software (Aspen Plus V12) for designing and optimising a distillation sequence.

reinforcement-learning Reinforcement Learning (RL)