# Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization

arXiv: https://arxiv.org/abs/2312.10194 · HTML: https://mlsanity.com/paper/2312.10194/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 15 of 15)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Reinforcement Learning for Combinatorial Optimization: A Survey](https://mlsanity.com/paper/2003.03600/) (2020)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation](https://mlsanity.com/paper/1908.08342/) (2019)
- [End-to-End Constrained Optimization Learning: A Survey](https://mlsanity.com/paper/2103.16378/) (2021)
- [Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1610.02707/) (2016)
- [Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1911.04936/) (2019)
- [Redeeming Intrinsic Rewards via Constrained Optimization](https://mlsanity.com/paper/2211.07627/) (2022)
- [Constrained Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/2006.11984/) (2020)
- [Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization](https://mlsanity.com/paper/2305.05812/) (2023)
- [Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem](https://mlsanity.com/paper/2103.06380/) (2021)

## Built on by (most cited first, 5 of 5)

- [Surpassing legacy approaches to PWR core reload optimization with single-objective Reinforcement learning](https://mlsanity.com/paper/2402.11040/) (2024)
- [Nuclear Microreactor Control with Deep Reinforcement Learning](https://mlsanity.com/paper/2504.00156/) (2025)
- [Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization](https://mlsanity.com/paper/2512.16032/) (2025)
- [Techno-economic optimization of a heat-pipe microreactor, part II: multi-objective optimization analysis](https://mlsanity.com/paper/2601.20079/) (2026)
- [MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications](https://mlsanity.com/paper/2604.26973/) (2026)

## Abstract (arXiv)

A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL), has been developed to address the challenges posed by multi-objective problems, particularly in the field of engineering where the evaluation of candidate solutions can be time-consuming. PEARL distinguishes itself from traditional policy-based multi-objective Reinforcement Learning methods by learning a single policy, eliminating the need for multiple neural networks to independently solve simpler sub-problems. Several versions inspired from deep learning and evolutionary techniques have been crafted, catering to both unconstrained and constrained problem domains. Curriculum Learning is harnessed to effectively manage constraints in these versions. PEARL's performance is first evaluated on classical multi-objective benchmarks. Additionally, it is tested on two practical PWR core Loading Pattern optimization problems to showcase its real-world applicability. The first problem involves optimizing the Cycle length and the rod-integrated peaking factor as the primary objectives, while the second problem incorporates the mean average enrichment as an additional objective. Furthermore, PEARL addresses three types of constraints related to boron concentration, peak pin burnup, and peak pin power. The results are systematically compared against conventional approaches. 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. It also outperforms the classical approach across multiple performance metrics, including the Hyper-volume.