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Found 5 papers, 1 papers with code
Surpassing legacy approaches and human intelligence with hybrid single- and multi-objective Reinforcement Learning-based optimization and interpretable AI to enable the economic operation of the US nuclear fleet
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
Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization
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
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization
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.
H2-Golden-Retriever: Methodology and Tool for an Evidence-Based Hydrogen Research Grantsmanship
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.
NEORL: NeuroEvolution Optimization with Reinforcement Learning
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.
