@article{Song2026, 
author = {Xiaobin Song and Siyuan Bai and Da-Wei Wang and Hanxiao Tao and Xizhe Wang and Rebing Wu and Benben Jiang},
title = {Reinforcement learning for charging optimization of inhomogeneous Dicke quantum batteries},
year = {2026},
journal = {Cybernetics and Intelligence},
volume = {1},
number = {2},
pages = {9390012},
keywords = {reinforcement learning (RL), partial observability, inhomogeneous Dicke quantum batteries, charging optimization},
url = {https://www.sciopen.com/article/10.26599/CAI.2026.9390012},
doi = {10.26599/CAI.2026.9390012},
abstract = {Charging optimization is a key challenge to the implementation of quantum batteries, particularly under inhomogeneity and partial observability. This paper employs reinforcement learning to optimize piecewise-constant charging policies for an inhomogeneous Dicke battery. We systematically compare policies across four observability regimes, from full-state access to experimentally accessible observables (energies of individual two-level systems (TLSs), first-order averages, and second-order correlations). Simulation results demonstrate that full observability yields near-optimal ergotropy with low variability, while under partial observability, access to only single-TLS energies or energies plus first-order averages lags behind the fully observed baseline. However, augmenting partial observations with second-order correlations recovers most of the gap, reaching 94%–98% of the full-state baseline. The learned schedules are nonmyopic, trading temporary plateaus or declines for superior terminal outcomes. These findings highlight a practical route to effective fast-charging protocols under realistic information constraints.}
}