## 5 Performance Evaluation
Thus we have observed that while changing the \(\gamma\) value to 0.5 and at \(\alpha\) value of 0.5 we have gained the optimality with minimum stretch described and illustrated in Figure 7a-d  
Figure 7: Stretch reduction and routing optimization progress of ISO at \(\gamma\) = 0.5: **(a)** Accumulated reward gain; **(b)** Average stretch gain; **(c)** Reward earned per episode; **(d)** Total hops/routers covered per episode. with less overall average stretch, average hop count, stretch per episode, and hop count at each step, respectively. In Figure 8a-d we have observed the results while the \(\gamma\) is 0.9 with \(\alpha\) varying to 0.1, 0.5 and 0.9. Figure 8a evaluates and illustrates the behavior of the agent when the \(\gamma\) value is increased to 0.5. It illustrates that the average stretch at \(\alpha\) value of 0.5 outperforms in comparison to other \(\alpha\) values of 0.1 and 0.9 and provides optimal and better stretch. Moreover, the average hop count between the AR/VR user and content router where the content has been identified is illustrated in Figure 8b. In Figure 8c we have illustrated the stretch incurred at one episode when the agent has completed one episode. We have also illustrated at different \(\alpha\) values of 0.1, 0.5, and 0.9 when \(\gamma\) value is constant at 0.9. The average steps shows the corresponding hop count while Figure 8a illustrates the average stretch. Therefore, Figure 8a,b are describing each other for example, when the average reward is eight at the 150 th iteration, then the number of hops taken by the agent from consumer to the producer is 2. Therefore, the agent has been passed two routers while choosing the optimal path. In Figure 8c, we have illustrated that instant reward incurred at each episode the consumer has requested the content, and Figure 8d represents the total hops the interest packet has traveled. At the same time, the discounted factor is increased to 0.5 and observing the agent's behavior at different \(\alpha\) values. Thus we have observed that while changing the \(\gamma\) value to 0.5. At \(\alpha\) value of 0.5, we have obtained the optimality i.e., the minimum stretch described as illustrated in Figure 8a-d with less overall average stretch, average hop count, stretch per episode, and hop count at each step, respectively. We have compared our results while changing the \(\gamma\) value to 0.9. Figure 8: Stretch reduction and routing optimization progress of Q-learning agent at \(\gamma\) = 0.9: (**a**) Accumulated reward gain; (**b**) Average stretch gain; (**c**) Reward earned per episode; (**d**) Total hops/routers covered per episode. The whole performance of the RL agent has been summarized in Table 3. The convergence and optimal routing path attained at a specific episode given the corresponding values of \(\alpha\) and \(\gamma\) of an agent are recorded. It can be observed that when \(\gamma\) = 0.5, we have achieved a better and faster optimal routing path. We have also illustrated the link delay of our proposed strategy in comparison with the random routing and HARP in Figure 9. We have kept the link delay between two content routers of 10 ms. Therefore, as our proposed algorithm converges towards optimal stretch in comparison to random routing and HARP. The average of random routing evaluated to 30.163 ms. Similarly, the HARP is converging at 30 ms. Furthermore, the proposed strategy converges to 20 ms. Therefore, our proposed strategy is 33.69% efficient and better in performance than that of random routing. Similarly, the proposed strategy is 33.33% good in performance in comparison to HARP.