###### Abstract  
The fifth-generation (5G) mobile network services are currently being made available for different use case scenarios like enhanced mobile broadband, ultra-reliable and low latency communication, and massive machine-type communication. The ever-increasing data requests from the users have shifted the communication paradigm to be based on the type of the requested data content or the so-called information-centric networking (ICN). The ICN primarily aims to enhance the performance of the network infrastructure in terms of the stretch to opt for the best routing path. Reduction in stretch merely reduces the end-to-end (E2E) latency to ensure the requirements of the 5G-enabled tactile internet (TI) services. The foremost challenge tackled by the ICN-based system is to minimize the stretch while selecting an optimal routing path. Therefore, in this work, a reinforcement learning-based intelligent stretch optimization (ISO) strategy has been proposed to reduce stretch and obtain an optimal routing path in ICN-based systems for the realization of 5G-enabled TI services. A Q-learning algorithm is utilized to explore and exploit the different routing paths within the ICN infrastructure. The problem is designed as a Markov decision process and solved with the help of the Q-learning algorithm. The simulation results indicate that the proposed strategy finds the optimal routing path for the delay-sensitive haptic-driven services of 5G-enabled TI based upon their stretch profile over ICN, such as the augmented reality /virtual reality applications. Moreover, we compare and evaluate the simulation results of proposed ISO strategy with random routing strategy and history aware routing protocol (HARP). The proposed ISO strategy reduces 33.33% and 33.69% delay as compared to random routing and HARP, respectively. Thus, the proposed strategy suggests an optimal routing path with lesser stretch to minimize the E2E latency. ICN; 5G; reinforcement learning; markov decision process; AR/VR; stretch reduction; tactile internet +
Footnote †: 26 June 2021  
are getting more attention in 5G-enabled TI [9]. Moreover, the TI applications and the application specific requirements and characteristics are illustrated in Table 1.  
The 5G network is an application-driven network infrastructure [11]. The research community has designed diverse network functionalities that enable 5G to meet the rapidly increasing demand for TI applications. The Multi-access edge computing (MEC) paradigm is one of the best key enablers of URLLC in the 5G ecosystem [12]. It brings the cloud-like capabilities at the edge of the network with limited abilities, i.e., caching, communication, control, and computation. The edge computing framework for 5G and beyond networks has three tiers, i.e., cloud, core, and edge devices [13]. Base station acts as an edge device for MEC-based 5G infrastructure. The massive increase in data requests results in a significant load on the network [14]. Thus, it is essential to offload some data requests and services at the edge of the network [15]. The edge device filters, parses, processes, and forwards data requests to the core network [16]. The MEC enables resource utilization, task offloading and management, content, and cache management that improves the QoE, QoS, and QoT for 5G at the edge of the network [17]. The MEC also lowers the traffic overhead at the core network [18]. Therefore, MEC is the driving factor to enable URLLC, high bandwidth, location awareness, and contextual services at the edge of the network [19]. Hence, MEC-based 5G is one of the key enablers to realize TI and allow haptic-driven AR/VR users to access the concerned application data from edge devices [20; 21]. Therefore, the current MEC-based 5G system provides URLLC services for haptic-driven AR/VR applications [22; 23]. Edge caching is the key enabler to cache the most requested content using information-centric networking (ICN), such as videos, close to users [24].