## 6 Results  
The results obtained from the implementation of the model in Figure 5 were the product of three different simulations. The user preferences obtained for each simulation are shown in Table 4. These simulations correspond to the user profile acquisition stage, simulating that three users played during 5 min trials and defined the parameters of speed, music, and the number of obstacles. These tests allowed validating the implementation within the virtual environment of the RL algorithm. Once the user preference acquisition stage was completed. The Q-learning algorithm through a Q-matrix--two-dimensional array of the maximum rewards obtained from switching from one state \(S_{t}\) to \(S_{t+1}\)--adjusts the transition between states as a function of a reward matrix (Equation (3)). The reward matrix was obtained for all the simulations, as presented in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Simulation**} & \multirow{2}{*}{**State**} & \multicolumn{4}{c}{**Parameters**} \\ \cline{3-5}  & & **Speed** & **Music** & **Number of Obstacles** \\ \hline \multirow{5}{*}{1} & \(S_{0}\) & least preferred (S2) & least preferred (M1) & least preferred (O2) \\  & \(S_{1}\) & least preferred (S2) & neutral preferred (M2) & least preferred (O2) \\  & \(S_{2}\) & least preferred (S2) & neutral preferred (M2) & neutral preferred (O1) \\  & \(S_{3}\) & neutral preferred (S3) & neutral preferred (M2) & neutral preferred (O1) \\  & \(S_{4}\) & most preferred (S1) & neutral preferred (M2) & neutral preferred (O1) \\  & \(S_{5}\) & most preferred (S1) & most preferred (M3) & neutral preferred (O1) \\  & \(S_{6}\) & most preferred (S1) & most preferred (M3) & most preferred (O3) \\ \hline \multirow{5}{*}{2} & \(S_{0}\) & least preferred (S1) & least preferred (M1) & least preferred (O1) \\  & \(S_{1}\) & least preferred (S1) & neutral preferred (M3) & least preferred (O2) \\  & \(S_{2}\) & least preferred (S1) & neutral preferred (M3) & neutral preferred (O2) \\  & \(S_{3}\) & neutral preferred (S2) & neutral preferred (M3) & neutral preferred (O2) \\  & \(S_{4}\) & most preferred (S3) & neutral preferred (M3) & neutral preferred (O2) \\  & \(S_{5}\) & most preferred (S3) & most preferred (M1) & neutral preferred (O2) \\  & \(S_{6}\) & most preferred (S3) & most preferred (M1) & most preferred (O3) \\ \hline \multirow{5}{*}{3} & \(S_{0}\) & least preferred (S3) & least preferred (M2) & least preferred (O3) \\  & \(S_{1}\) & least preferred (S3) & neutral preferred (M1) & least preferred (O3) \\  & \(S_{2}\) & least preferred (S3) & neutral preferred (M1) & neutral preferred (O3) \\  & \(S_{3}\) & neutral preferred (S2) & neutral preferred (M1) & neutral preferred (O3) \\  & \(S_{4}\) & most preferred (S1) & neutral preferred (M1) & neutral preferred (O3) \\  & \(S_{5}\) & most preferred (S1) & most preferred (M3) & neutral preferred (O3) \\  & \(S_{6}\) & most preferred (S1) & most preferred (M3) & most preferred (O1) \\ \hline \hline \end{tabular}  
* Corresponding satisfaction states: \(S_{0}\), extremely unlikable; \(S_{1}\), unlikable; \(S_{2}\), more or less unlikable; \(S_{3}\), neutral; \(S_{4}\), more or less likable; \(S_{5}\), likable; \(S_{6}\) extremely likable.  
\end{table} 
Table 4: Relationship of simulations with 3 different types of user preferences. \[\mathbf{R}= \begin{bmatrix}s_{0}&s_{1}&s_{2}&s_{3}&s_{4}&s_{5}&s_{6}\\ -5&0&0&-1&-1&-1&-5\\ -1&-1&0&0&-1&-1&-1\\ -1&-1&0&0&-1&-1&-1\\ -1&-1&-1&0&0&-1&-1\\ -5&-1&-1&-1&0&0&0\\ -1&-1&-1&-1&0&0&10\\ -5&-1&-1&-1&1&0&10\\ \end{bmatrix}