With the massive growth of the Internet of Things (IoT), novel wireless communication technologies have emerged to address the long-range, lowcost, and low-power consumption requirements of the IoT applications. In this context, the Low Power Wide Area Networks (LPWANs) have appeared, offering different solutions that meet the IoT applications requirements mentioned before. Among the existing LPWAN solutions, LoRaWAN has stood out for receiving significant attention from both industry and academia in recent years. Although LoRaWAN offers a compelling combination of long-range and low-power consumption data transmissions, it still faces several challenges in terms of reliability and scalability. However, due to its open-source nature and the flexibility of the modulation scheme it is based on (Long Range (LoRa) modulation allows the adjustment of spreading factors and transmit power), LoRaWAN also offers important possibilities for improvements. This thesis takes advantage of the appropriateness of the Reinforcement Learning (RL) algorithms for solving decision-making tasks, and use them to dynamically adjust the transmission parameters of LoRaWAN end devices. The proposed system, called RL-LoRa, shows significant improvements in terms of reliability and scalability when compared with LoRaWAN. Specifically, it decreases the average Packet Error Ratio (PER) of LoRaWAN by 15 percent, which can further increase the network scalability.