no code implementations • 16 Feb 2024 • Paul Seurin, Koroush Shirvan
To advance the state-of-the-art in core reload patterns, we have developed methods based on Deep Reinforcement Learning.
Multi-Objective Reinforcement Learning Stochastic Optimization
no code implementations • 15 Dec 2023 • Paul Seurin, Koroush Shirvan
Notably, PEARL, specifically the PEARL-NdS variant, efficiently uncovers a Pareto front without necessitating additional efforts from the algorithm designer, as opposed to a single optimization with scaled objectives.
Multi-Objective Reinforcement Learning reinforcement-learning
no code implementations • 9 May 2023 • Paul Seurin, Koroush Shirvan
This work presents a first-of-a-kind approach to utilize deep RL to solve the loading pattern problem and could be leveraged for any engineering design optimization.
no code implementations • 16 Nov 2022 • Paul Seurin, Olusola Olabanjo, Joseph Wiggins, Lorien Pratt, Loveneesh Rana, Rozhin Yasaei, Gregory Renard
The Knowledge Graph module was used for the generation of meaningful entities and their relationships, trends and patterns in relevant H2 papers, thanks to an ontology of the hydrogen production domain.
1 code implementation • 1 Dec 2021 • Majdi I. Radaideh, Katelin Du, Paul Seurin, Devin Seyler, Xubo Gu, Haijia Wang, Koroush Shirvan
NEORL offers a global optimization interface of state-of-the-art algorithms in the field of evolutionary computation, neural networks through reinforcement learning, and hybrid neuroevolution algorithms.