Reinforcement learning optimizes polarization control for APOLLO cryogenic target (arXiv:2610.02452v1)
arXiv:2610.02452v1 presents a data-driven control framework that combines surrogate modeling (Gaussian processes and multilayer perceptrons) with a reinforcement learning agent to optimize microwave-frequency tuning for dynamically polarized targets using operational data from the APOLLO cryogenic system. The paper finds Gaussian processes yield calibrated uncertainty and help detect out-of-distribution regimes, embeds a GP approximation into a simulator, trains an RL agent with a lower-confidence-bound reward balancing performance and uncertainty, and reports roughly a 2× improvement compared with operator actions.