## 1 Introduction
Moreover, several works have also recently considered the use of MC in contexts that are relevant for future beyond 5G and 6G systems, such as the communication over mmWave and THz frequency bands [8], the multi-connectivity between a base station and other UEs acting as relays [9], and hybrid satellite and terrestrial networks [10], in which for example the joint use of a reconfigurable intelligent surface and a base station are used to strengthen satellite signals in [11]. From an algorithmic perspective, the literature has considered different problems in relation to MC, such as resource allocation [12; 13], cell and RAT selection [14; 15], and traffic split [16; 17; 18; 19]. Concerning resource allocation, a Smart Aggregated RAT Access (SARA) strategy is proposed in [12] for joint RAT selection and resource allocation in a scenario with cellular base stations and Wi-Fi access points. The solution makes use of a Semi Markov Decision Process (SMDP)-based hierarchical decision framework (HDF). In [13] the optimization problem of resource allocation in a MC scenario with 5G and LTE is formulated. Then, a solution based on two heuristic algorithms is proposed, namely a base station selection algorithm performed by the UE and a resource block allocation algorithm executed by the base station. The problem of secondary base station selection in MC with 5G/LTE is addressed in [14], which presents different algorithms aimed at improving robustness and performance while minimizing the energy consumption. In turn, [15] considers an LTE/WiFi scenario and proposes a network switching strategy based on a Markov Decision Process and a value iteration algorithm to determine the set of RATs that a user is connected to in each handover window. The problem of traffic split between different RATs is considered in [16] focusing on an LTE/Wi-Fi scenario. The paper assumes a heterogeneous network controlled by a single coordinating node that collects relevant information, decides on the best choice of RAT for all users, and advises on the actual amount of radio resources that every user may utilize on each technology. The problem is formulated analytically, and a solution based on the weighted max-min algorithm is proposed. In [17] the problem of traffic split between the master and the secondary eNB in LTE with dual connectivity is modelled as a Constrained Markov Decision Process, and a solution based on the Lagrangian approach is proposed. Similarly, [18] considers a scenario with 5G-LTE multi-connectivity, and makes use of Lagrangian Dual Decomposition to determine the fraction of traffic transmitted through each cell that maximizes the goodput, whereas [19] formulates a PDCP split bearer decision problem that decides whether and how to split the traffic across multiple cells in order to meet the bandwidth requirements of user services and proposes a heuristic solution to solve the problem. This paper addresses the traffic split multi-connectivity problem in multi-RAT scenarios. The target is to determine a policy to optimally distribute the traffic of a UE across the different RATs and cells by fulfilling the QoS requirements while minimizing the resource consumption of the UE and ensuring that no overload situations arise in the involved cells. The novelty of the paper with respect to prior works dealing with traffic split [16; 17; 18; 19] is the use of Deep Reinforcement Learning (DRL), and in particular the Deep Q-Network (DQN) technique [20], in order to learn the traffic split policy to be applied on a per UE basis so that the amount of bandwidth used by a UE is minimized while at the same time providing the required bit rate and avoiding overload situations in the involved cells. To the authors' best knowledge, the use of DQN has not been considered by other researchers in the context of traffic split for MC.