Feedback control of #openquantumsystems is of fundamental importance for practical applications in various contexts, ranging from #quantumcomputation to quantum error correction and quantum metrology. Its use in the context of #thermodynamics further enables the study of the interplay between information and energy. However, deriving optimal #feedback control strategies is highly challenging, as it involves the optimal control of open quantum systems, the stochastic nature of quantum #measurement, and the inclusion of policies that maximize a long-term time- and trajectory-averaged goal. In this work, we employ a #reinforcementlearning approach in #machinelearning to automate and capture the role of a quantum @Maxwell's demon: the #agent takes the literal role of discovering optimal feedback control strategies in qubit-based systems that maximize a trade-off between measurement-powered cooling and measurement efficiency. Considering weak or projective quantum measurements, we explore different regimes based on the ordering between the thermalization, the measurement, and the unitary feedback timescales, finding different and highly non-intuitive, yet interpretable, strategies. In the thermalization-dominated regime, we find strategies with elaborate finite-time thermalization protocols conditioned on measurement outcomes. In the measurement-dominated regime, we find that optimal strategies involve adaptively measuring different qubit observables reflecting the acquired information, and repeating multiple weak measurements until the quantum state is "sufficiently pure", leading to random walks in state space. Finally, we study the case when all timescales are comparable, finding new feedback control strategies that considerably outperform more intuitive ones. We discuss a two-qubit example where we explore the role of entanglement and conclude discussing the scaling of our results to quantum many-body systems.