Search Results for author:
Found 6 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.
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.
Machine learning-assisted surrogate construction for full-core fuel performance analysis
Such one-way coupling is result of the high cost induced by the full-core fuel performance analysis, which provides more realistic and accurate prediction of the core-wide response than the "peak rod" analysis.
Improving Intelligence of Evolutionary Algorithms Using Experience Share and Replay
We propose PESA, a novel approach combining Particle Swarm Optimisation (PSO), Evolution Strategy (ES), and Simulated Annealing (SA) in a hybrid Algorithm, inspired from reinforcement learning.
