## 7 Experiments and Results  
In this section, we describe our experiments with six different models on the datasets. The best validation accuracies of the various methods on each of the datasets are reported in Table 3. The six classification models we used in our experiments are described below. The first classifier is the HQL RRF. We performed a grid search of \(\alpha\) values of 0.7, 0.8, and 0.9 and \(\gamma\) values of 0.1, 0.2, and 0.3. The \(\epsilon\) value was updated by the the rule \(\epsilon=\epsilon\cdot 0.998\) after each learning episode. The Q-Matrix obtained with the HQL method is used to generate the sequence \(S\) of DTs to generate the RRFs. Initially, \(S\) contains the 2 DTs that correspond to the state and action of the maximum q-value in the Q-Matrix. After the addition of the 2 DTs, the Q-Matrix cell with the maximum q-value is set to zero and the reconstruction recursively continues from the last action performed. Recall that the last action specifies the number of the last DT added. Specifically, the reconstruction goes to the corresponding state and performs the action of adding the DT whose number is specified in the column of the maximum q-value for the row. After the DT is added, the Q-Matrix cell with the maximum q-value is set to 0. The process continues until the number of the added DTs reaches 200. The HQL RRF was trained on the grayscaled images of all datasets. Let \(S=(T_{1},T_{2},...,T_{200})\) be the sequence of DTs added to the RRF according to the maximum q-values in the Q-Matrix. In other words, \(T_{1}\) is added first, then \(T_{2}\), etc. After the addition of each DT \(T_{j}\), the validation accuracy of the RRF, were it to be constructed from the DTs in \(S\), is computed. Thus, if the initial sequence \(S\) contains \(T_{1}\), then the validation accuracy \(v_{1}\) of the RRF that contains only \(T_{1}\) is computed and recorded. If \(T_{2}\) is the second DT in \(S\), then the validation accuracy of \(v_{2}\) of the RRF that contains \((T_{1},T_{2})\) is computed and recorded. Formally, the sequence \(V_{hql}=((S_{1},v_{1}),(S_{2},v_{2}),...,(S_{200},v_{200}))\) is computed, where \(S_{i}\) is the subsequence of \(S\) that starts with \(T_{1}\) and ends with \(T_{i}\). The HQL RRF is constructed from the pair \((S_{i},v_{i})\), where \(v_{i}\) is the maximum of \(v_{1},...,v_{200}\). Put another way, the HQL RRF is constructed from the minimal subsequence of DTs in \(V_{hql}\) that has the maximum validation performance. The second and third classifiers are the the DQL 1 and DQL 2 RRFs. We performed a grid search of \(\alpha\) values of 0.8, and 0.9 and \(\gamma\) values of 0.1 and 0.2. The \(\epsilon\) value was updated by the the rule \(\epsilon=\epsilon\cdot 0.998\) after each learning episode. The DQL 1 and DQL 2 RRFs were trained on the grayscaled images of all datasets. We call the fourth classifier the performance RF (PRF). To generate the PRF, the trained DTs are sorted in the descending order of their validation accuracies. Let this sequence be \(V_{pff}=((T_{1},v_{1}),...,(T_{200},v_{200}))\), where \(T_{i}\) is a DT and \(v_{1}\) is the maximum validation accuracy and \(v_{200}\) is the minimal accuracy. The sequence \(V_{pff}\) is used in construction of the PRF in the same way as the sequence \(V_{Ingl}\) is used in the construction of the HQL RRF. Put another way, the PRF is constructed from the minimal subsequence of DTs added in the order of the DTs in \(V_{prf}\) that has the maximum validation performance. The PRFs were trained on the grayscaled images of all datasets. The fifth classifier is the base case random forest classifier constructed with the RandomForestClassifier class of the scikit-learn library [21]. For each dataset, we trained the RFs with 50, 100, and 150 DTs using the Gini function both on the color and grayscale images of each dataset. These classifiers were trained on both grayscaled and color images of all datasets.