## 1 Introduction  
Due to the high complexity of managing the traffic on the network edge [1], centralized routing algorithms are more efficient than distributed ones. In fact, distributed routing algorithms, such as Border Gateway Protocol (BGP) or Open Shortest Path First (OSPF) could have significant delays to both discover an operational failure on the used routing path and find an alternative reliable path. A centralized routing algorithm with a more complete knowledge of the routing state can solve routing issues quicker than their distributed counterparts. Along the centralized approaches to control the network resources, the Software-Defined Networking (SDN) paradigm has emerged in recent years as a very popular paradigm to reduce the cost of the network operation and maintenance, as well as to hide the network complexity from the high-level programmed algorithms. Ucoming networking scenarios are predicted to be highly volatile in terms of the network topology or network load, among other aspects. Consequently, it is imperative that the operation of centralized algorithms becomes automatically adjusted via learning agents to any unexpected changes in the network operation. The authors in [2] analyze previous work on Machine Learning (ML) techniques for routing optimization in SDN. In addition, ref. [3] revises the available literature focused on ML research opportunities and evolution in distinct areas of networking. A tutorial about the application of ML algorithms for wireless networks is available in [4]. For usage scenarios with high quantities of available data about the system operation, Deep Learning (DL) methods are becoming a popular alternative to traditional ML methods, because DL is able to automatically extract high-level featuresand corresponding correlations from the input data, avoiding the need for the manual engineering of these features, which is often required by traditional ML methods [5]. An important family of ML algorithms tailored for sequential decision making is known as Reinforcement Learning (RL) [6]. Interestingly, little work has been carried out considering RL for dynamic routing controlled by SDN systems [2]. Thus, we identified the pertinence of carrying out further research on learning agents capable of enhancing the performance of SDN-based systems. In recent years, Multi-Armed Bandit (MAB) solutions [7; 8], which are a form of RL, have attracted the attention of many scholars due to their simple implementation and high flexibility, enabling agent learning in heterogeneous scenarios [9], such as recommendation systems, medicament trials, anomaly detection, and network configuration. MAB algorithms address a fundamental RL problem, which is balancing exploration and exploitation when choosing between uncertain actions: should the agent explore an action that seems to be less rewarding with the expectation that one might be wrong about it, or simply opt for the action that appears most rewarding, assuming one's current knowledge is sufficiently accurate? To robustly handle the highly dynamic behavior of communication networks, we devised and included in our agent a novel MAB algorithm, React-UCB, prepared to learn from reward distributions that are piecewise-stationary and may abruptly change at unknown moments. React-UCB combines the following features: _optimism in the face of uncertainty_; discounted rewards to favor the present over the past; reset of reward estimates when abrupt path delay distribution changes are detected; reward correlations to transfer learning among paths; and suspended exploration after a good reward has been found to reduce the agent's accumulated regret. The proposal's main aim of finding the minimum delay path can be also seen as an intelligent way of enhancing the network operation in terms of its robustness against congestion scenarios, because when a network device is highly congested, due to internal output buffering delays, it negatively increases the transmission message delay from that device to their network neighbors.