Surpassing Legacy Approaches to PWR Core Reload Optimization with Single-Objective Reinforcement Learning
Name
UNSE_A_2488702_O.pdf
Description
Published version
Size
32.31 MB
Format
Adobe PDF
Checksum (MD5)
8307365de8f7a03eae58ae71c68e9155
Author(s)
Seurin, Paul
Shirvan, Koroush
Journal
Nuclear Science and Engineering
Publisher
Informa UK Limited
Citation
Seurin, P., & Shirvan, K. (2025). Surpassing Legacy Approaches to PWR Core Reload Optimization with Single-Objective Reinforcement Learning. Nuclear Science and Engineering, 1–32.
Version
Final published version
Abstract
Optimizing the fuel cycle cost through the optimization of nuclear reactor core loading patterns (LPs) involves multiple objectives and constraints, leading to a vast number of candidate solutions that cannot be explicitly solved. To advance the state of the art in core reload patterns, we have developed methods based on deep Reinforcement Learning (RL) for both single- and multi-objective optimization. Our previous research laid the groundwork for these approaches and demonstrated their ability to discover high-quality patterns within a reasonable time frame. On the other hand, Stochastic Optimization (SO) approaches are commonly used in the literature, but there is no rigorous explanation that shows which approach is better in which scenario. In this paper, we demonstrate the advantage of our RL-based approach, specifically using Proximal Policy Optimization (PPO) against the most commonly used SO-based methods: Genetic Algorithm, Parallel Simulated Annealing with mixing of states, and Tabu Search, as well as an ensemble-based method, i.e. the Prioritized replay Evolutionary and Swarm Algorithm. We found that the LP scenarios derived in this paper are amenable to a global search to identify promising research directions rapidly but then need to transition into a local search to exploit these directions efficiently and prevent getting stuck in local optima. PPO adapts its search capability via a policy with learnable weights, allowing it to function as both a global search method and a local search method. Subsequently, we compared all algorithms against PPO in long runs, which exacerbated the differences seen in the shorter cases. Overall, the work demonstrates the statistical superiority of PPO compared to the other considered algorithms.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
10.1080/00295639.2025.2488702
