## 3 Results and Analysis  
Rewards were divided into different types to better reflect the assessment of the environment. Therefore, the total system reward is the sum of several independent rewards. Each of them represents the evaluation of a feature from the optimization system. In the reward creation process, we defined a range of importance between them so that the ones used to balance training do not outnumber those most important. The reward function that returns the maximum value is the MOS one (QoE metric), since it better represents the behavior of the system. The bitrate function also helps to determine possible problems in the flow, second in reward importance, by possible bitrate loss along the way. Therefore, in this bitrate reward function, we defined it as the difference between input and output bitrates. In order to equilibrate the model, the other two rewards were developed as the development progressed in time. Smooth reward focuses on avoiding abrupt changes between profiles, while profile rewards overcomes conservative behavior for medium profiles. This is produced because medium profiles do not offer maximum reward for MOS, because of using lower bitrates, avoiding possible image problems. These reward functions included the predicted action parameter, the current one and the last one. In other words, \(a\)' represents the previous action, so that the current reward evaluates the evolution over the preceding profile, and \(a\) represents the action chosen at that step. \[ \begin{split} RewardMOS&=\pm 2*e^{1.5s(\pm(MOS-25))} \\ RewardBitrate&=-e^{2*(1+\frac{|bitrate_{in}-bitrate_{out}|}{| bitrate_{in}|-|bitrate_{out}|})}\\ RewardSmooth&=12*\ln\frac{|a^{ \prime}-a+2|}{|a^{\prime}+1|-|a+1|}\\ RewardProfile&=2^{4-a}\\ TotalReward&=Rvd\_MOS+Rwd\_Bitrate+Rvd\_ Smooth+Rwd\_Profile\end{split}  \tag{3}\]Finally, to offer some conclusions and based on previous test training, we present the model that returns the best increase of the reward function while maintaining a good QoE. Model validation is unique to the simulated environment, but it would be easy to apply it to other clients in another environment, as long as the same data are included for the state set, and the environment rewards correctly define the new environment. In addition, the fundamental requirement is that the action policy is subject to the network profiles mentioned above with a defined set of bitrates. An important point is to define the number of iterations to average the rewards for each step. It marks the line when individual iterations reward is accumulated. Each training step was set by 25 iterations, allowing enough accumulated reward to differentiate progress over time and offer learning to the model. In this configuration, iterations represent the optimizer's actions cycle and the effect produced on the system. In total training, 12,500 iterations were performed, which took approximately 24 h.  
Figure 4 shows how negative values are returned on average in the initial steps, but after training, the average reward function improved by more than 300% from the beginning, indicating that the algorithm optimizes properly following the reward function, which delivers a better score with training. Reward graphic presents sufficient justification for the demonstration of improvement over training. Good quality represents higher reward, but there is another aspect that can decrease the general reward: image artifacts. When the probe analyzes the content and detects enough image artifacts for bad visualization, the returned MOS is 1. Figure 5 on the left represents the histogram values of MOS during training. Although there is more representation for higher quality values in general, there is a large percentage of minimum quality values. In contrast, Figure 5 on the right side represents the same graphic histogram, but using the optimizer. Not only has the distribution moved to higher values, but there are also a few 1.0 MOS values.