  Reducing operation and maintenance costs is a key objective for advanced
reactors in general and microreactors in particular. To achieve this reduction,
developing robust autonomous control algorithms is essential to ensure safe and
autonomous reactor operation. Recently, artificial intelligence and machine
learning algorithms, specifically reinforcement learning (RL) algorithms, have
seen rapid increased application to control problems, such as plasma control in
fusion tokamaks and building energy management. In this work, we introduce the
use of RL for intelligent control in nuclear microreactors. The RL agent is
trained using proximal policy optimization (PPO) and advantage actor-critic
(A2C), cutting-edge deep RL techniques, based on a high-fidelity simulation of
a microreactor design inspired by the Westinghouse eVinci\textsuperscript{TM}
design. We utilized a Serpent model to generate data on drum positions, core
criticality, and core power distribution for training a feedforward neural
network surrogate model. This surrogate model was then used to guide a PPO and
A2C control policies in determining the optimal drum position across various
reactor burnup states, ensuring critical core conditions and symmetrical power
distribution across all six core portions. The results demonstrate the
excellent performance of PPO in identifying optimal drum positions, achieving a
hextant power tilt ratio of approximately 1.002 (within the limit of $<$ 1.02)
and maintaining criticality within a 10 pcm range. A2C did not provide as
competitive of a performance as PPO in terms of performance metrics for all
burnup steps considered in the cycle. Additionally, the results highlight the
capability of well-trained RL control policies to quickly identify control
actions, suggesting a promising approach for enabling real-time autonomous
control through digital twins.
