Surpassing legacy approaches and human intelligence with hybrid single-
  and multi-objective Reinforcement Learning-based optimization and
  interpretable AI to enable the economic operation of the US nuclear fleet
Paul Seurin Koroush Shirvan
The nuclear sector represents the primary source of carbon-free energy in the
United States. Nevertheless, existing nuclear power plants face the threat of
early shutdowns due to their inability to compete economically against
alternatives such as gas power plants. Optimizing the fuel cycle cost through
the optimization of core loading patterns is one approach to addressing this
lack of competitiveness. However, this optimization task involves multiple
objectives and constraints, resulting in a vast number of candidate solutions
that cannot be explicitly solved. While stochastic optimization (SO)
methodologies are utilized by various nuclear utilities and vendors for fuel
cycle reload design, manual design remains the preferred approach. To advance
the state-of-the-art in core reload patterns, we have developed methods based
on Deep Reinforcement Learning. Previous research has laid the groundwork for
this approach and demonstrated its ability to discover high-quality patterns
within a reasonable timeframe. However, there is a need for comparison against
legacy methods to demonstrate its utility in a single-objective setting. While
RL methods have shown superiority in multi-objective settings, they have not
yet been applied to address the competitiveness issue effectively. In this
paper, we rigorously compare our RL-based approach against the most commonly
used SO-based methods, namely Genetic Algorithm (GA), Simulated Annealing (SA),
and Tabu Search (TS). Subsequently, we introduce a new hybrid paradigm to
devise innovative designs, resulting in economic gains ranging from 2.8 to 3.3
million dollars per year per plant. This development leverages interpretable
AI, enabling improved algorithmic efficiency by making black-box optimizations
interpretable. Future work will focus on scaling this method to address a
broader range of core designs.