The nuclear fuel loading pattern optimization problem has been studied since
the dawn of the commercial nuclear energy industry. It is characterized by
multiple objectives and constraints, with a very high number of candidate
patterns, which makes it impossible to solve explicitly. Stochastic
optimization methodologies are used by different nuclear utilities and vendors
to perform fuel cycle reload design. Nevertheless, hand-designed solutions
continue to be the prevalent method in the industry. To improve the
state-of-the-art core reload patterns, we aim to create a method as scalable as
possible, that agrees with the designer's goal of performance and safety. To
help in this task Deep Reinforcement Learning (RL), in particular, Proximal
Policy Optimization is leveraged. RL has recently experienced a strong impetus
from its successes applied to games. This paper lays out the foundation of this
method and proposes to study the behavior of several hyper-parameters that
influence the RL algorithm via a multi-measure approach helped with statistical
tests. The algorithm is highly dependent on multiple factors such as the shape
of the objective function derived for the core design that behaves as a fudge
factor that affects the stability of the learning. But also an
exploration/exploitation trade-off that manifests through different parameters
such as the number of loading patterns seen by the agents per episode, the
number of samples collected before a policy update, and an entropy factor that
increases the randomness of the policy trained. Experimental results also
demonstrate the effectiveness of the method in finding high-quality solutions
from scratch within a reasonable amount of time. Future work must include
applying the algorithms to wide range of applications and comparing them to
state-of-the-art implementation of stochastic optimization methods.

本研究致力于通过使用深度强化学习中的近端策略优化算法，研究多重约束下核燃料 loading pattern 的最佳优化方法，并通过实验结果展示其优越性。