  In recent years, 5G is widely used in parallel with IoT networks to enable
massive data connectivity and exchange with ultra-reliable and low latency
communication (URLLC) services. The internet requirements from user's
perspective have shifted from simple human to human interactions to different
communication paradigms and information-centric networking (ICN). ICN
distributes the content among the users based on their trending requests. ICN
is responsible not only for the routing and caching but also for naming the
network's content. ICN considers several parameters such as cache-hit ratio,
content diversity, content redundancy, and stretch to route the content. ICN
enables name-based caching of the required content according to the user's
request based on the router's interest table. The stretch shows the path
covered while retrieving the content from producer to consumer. Reduction in
path length also leads to a reduction in end-to-end latency and better data
rate availability. ICN routers must have the minimum stretch to obtain a better
system efficiency. Reinforcement learning (RL) is widely used in networks
environment to increase agent efficiency to make decisions. In ICN, RL can aid
to increase caching and stretch efficiency. This paper investigates a stretch
reduction strategy for ICN routers by formulating the stretch reduction problem
as a Markov decision process. The evaluation of the proposed stretch reduction
strategy's accuracy is done by employing Q-Learning, an RL technique. The
simulation results indicate that by using the optimal parameters for the
proposed stretch reduction strategy.
