<ul><li>This study explores the application of deep reinforcement learning for real-time drum control in nuclear microreactors.</li><li>Deep reinforcement learning controllers demonstrate similar or better load-following performance compared to traditional PID control.</li><li>RL agents can reduce tracking error rate in short transients and maintain accuracy in longer, more complex load-following scenarios.</li><li>Multi-agent RL enables independent drum control and maintains reactor symmetry constraints without sacrificing performance.</li></ul>

Nuclear Microreactor Control with Deep Reinforcement Learning

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