Today’s office and home environments are moving towards more connected dig- ital infrastructures, meaning there are multiple heterogeneous devices that uses short-range communication to stay connected. Mobile phones, tablets, lap- tops, sensors, printers are examples of devices in such environments. From this, the Internet of Things (IoT) paradigm arises, and to enable it, energy efficient machine-to-machine (M2M) communications are needed. Our study will use Bluetooth Low Energy (BLE) technology for communication between devices, and it demonstrates the impact of routing algorithms in such networks. With the goal to increase the network lifetime, a distributed and dynamic Reinforce- ment Learning (RL) routing algorithm is proposed. The algorithm is based on a RL technique called Q-learning. Performance analysis is performed in different scenarios comparing the proposed algorithm against two static and centralized reference routing algorithms. The results show that our proposed RL routing algorithm performs better as the node degree of the topology increases. Com- pared to the reference algorithms the proposed algorithm can handle a higher load on the network with significant performance improvement, due to the dy- namic change of routes. The increase in network lifetime with 75 devices is 124% and 100 devices is 349%, because of the ability to change routes as time passes which is emphasized when the node degree increases. For 35, 55 and 75 devices the average node degrees are 2.21, 2.39 and 2.54. On a lower number of devices our RL routing algorithm performs nearly as good as the best refer- ence algorithm, the Energy Aware Routing (EAR) algorithm, with a decrease in network lifetime around 19% on 35 devices and 10% on 55 devices. A decrease in the network lifetime on lower number of devices is because of the cost for learning new paths is higher than the gain from exploring multiple paths. ; Dagens kontors- och hemmiljöer rör sig mot mer sammankopplad digital in-frastruktur, vilket innebär att det finns många ...