Surpassing legacy approaches to PWR core reload optimization with single-objective Reinforcement learning
作者: Paul Seurin, Koroush Shirvan
分类: cs.NE, cs.LG, physics.soc-ph
发布日期: 2024-02-16 (更新: 2024-07-14)
💡 一句话要点
基于强化学习的核反应堆核心重装优化方法
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)
关键词: 核反应堆优化 深度强化学习 近端策略优化 多目标优化 随机优化算法
📋 核心要点
- 现有的核反应堆核心重装优化方法通常面临多目标和约束的挑战，导致无法有效求解。
- 本文提出了一种基于深度强化学习的单目标优化方法，利用PPO算法实现全局与局部搜索的结合。
- 实验结果显示，PPO在长时间运行中表现出明显的统计优势，优于传统的随机优化算法。
📝 摘要（中文）
通过优化核反应堆核心装载模式来降低燃料循环成本涉及多个目标和约束，导致候选解决方案数量庞大，无法明确求解。为此，本文提出了基于深度强化学习（DRL）的方法，特别是使用近端策略优化（PPO），与传统的随机优化方法（如遗传算法、并行模拟退火和禁忌搜索）进行比较。研究表明，PPO在长时间运行中的统计优越性明显，能够有效地进行全局和局部搜索，快速识别有前景的研究方向并高效利用。整体上，本文为核反应堆核心重装优化提供了新的思路和方法。
🔬 方法详解
问题定义：本文旨在解决核反应堆核心重装优化中的多目标和约束问题，现有方法如随机优化在处理复杂性时存在局限性。
核心思路：提出基于深度强化学习的优化方法，特别是近端策略优化（PPO），通过可学习的权重调整搜索策略，实现全局与局部搜索的有效结合。
技术框架：整体架构包括数据预处理、模型训练和优化策略评估三个主要模块。首先，通过模拟生成候选装载模式，然后利用PPO进行训练，最后评估其在不同场景下的表现。
关键创新：PPO算法的引入使得优化过程能够动态调整搜索策略，克服了传统方法在局部最优解上的局限性，提升了全局搜索能力。
关键设计：在模型设计中，采用了特定的损失函数以平衡探索与利用，同时设置了适应性学习率以提高训练效率。
🖼️ 关键图片
📊 实验亮点
实验结果表明，PPO在长时间运行中相较于遗传算法、并行模拟退火和禁忌搜索等传统方法，表现出更高的优化效率和更优的解质量，统计显著性分析显示PPO的优势更加明显。
🎯 应用场景
该研究的潜在应用领域包括核能行业的燃料管理、核反应堆设计优化等。通过提高核心重装的优化效率，可以降低运营成本，提升安全性和经济性，具有重要的实际价值和未来影响。
📄 摘要（原文）
Optimizing the fuel cycle cost through the optimization of nuclear reactor core loading patterns 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 (DRL) for both single- and multi-objective optimization. Our previous research has 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 (GA), Parallel Simulated Annealing (PSA) with mixing of states, and Tabu Search (TS), as well as an ensemble-based method, Prioritized Replay Evolutionary and Swarm Algorithm (PESA). 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 and 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.