  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.
