LoRaWAN networks can involve large numbers of wireless devices relying on batteries to sense the environment and send data to gateways. A critical trade-off for transmission performance (packet delivery ratio) versus energy conservation (and hence, the device lifespan) appears when deciding the transmission parameters, in particular, the Spreading Factor (SF) to be used by each node. In this paper, we use lightweight reinforcement learning techniques, namely multi-armed bandits, for each node to select an appropriate SF, based on preferences regarding that trade-off. Unlike previous works on that topic, we relax some assumptions to aim at a realistic implementation: our solution does not assume immediate rewards, or that each device communicates with only one gateway. Additionally, we build explicit MAC commands for the method to work in practice and implement it in the ns-3 simulator using a state-of-the-art LoRaWAN module. We share the source code of our implementation and our simulation results. Those simulations show that when energy conservation is critical for IoT nodes, such lightweight learning algorithms outperform LoRaWAN's legacy Adaptive Data Rate algorithm, both in single- and multi-gateway scenarios.