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Batching is a well known technique to boost the throughput of Total Order Broadcast (TOB) protocols. Unfortunately, its manual configuration is not only a time consuming process, but also a very delicate one, as incorrect settings of the batching parameter can lead to severe performance degradation. In this paper we address precisely this issue, by presenting an innovative mechanism for self-tuning the batching level in TOB protocols. Our solution combines analytical modeling and reinforcement learning techniques, taking the best of these two worlds: drastic reductions of the learning time and the ability to correct inaccurate predictions by accumulating feedback from the operation of the system.