## 4 Discussion  
As can be seen in Figure 6, all tested configurations of NNs trained with the PPO algorithm established 100% effectiveness in solving the problem described in Section 2.6 after no more than \(15\cdot 10^{5}\) episodes. For an observer who visually compares individual Agents' actions, they behaved similarly in a virtual environment. At first, they made rotational movements until they did not find the Platform/Collectible object. They then moved toward it by jumping at the right moment, which allowed them to reach the Collectible object. However, each of these networks worked in a slightly different way. An important factor was that we used two feature extractors, \"Simple\" and \"Nature\", which differed in the number of layers and the resolution of the last convolution layer. Also, the data coming from the input vision sensor varied significantly in resolution, from \((64\times 64)\) up to \((192\times 192)\). Thus, it was natural that the feature image extractor that provided different input data determined the inference by the following fully connected neural layers with sigmoid activation functions (see Figure 2). Example performance visualizations of convolution neural with various input image resolution networks processed by semantic segmentation and GradCam for the same input image can be seen in Figures 7 and 8. Aware of a certain simplification, we call the area of the GradCAM map where the averaged gradient obtained the most significant value in this discussion the area observed by the Agent. In some cases, such an observable area was those portions of the map where the entire Platform or Collectible the Agent was aiming at was located. This situation happened in Figures 7c,g and 8e. However, this was not the general rule. Sometimes the observed area included only a portion of the target, for example, in Figures 7f and 8g, or the target and a portion of its surroundings, as in Figures 7d,e and 8b-d,f or did not observe the area at all and observed only its surroundings like in Figure 7b. By GradCAM's assumption, these  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{48\times 48}\) & \(\mathbf{56\times 56}\) & \(\mathbf{64\times 64}\) & \(\mathbf{96\times 96}\) & \(\mathbf{128\times 128}\) & \(\mathbf{192\times 192}\)  
areas were the most influential in separating images into the different classes of activity performed by the Agent. Based on the results in Tables 1 and 2, we can conclude that our proposed method allowed an efficient approximation of the decision made by the neural network. For the Naive Method, the accuracy of approximation was in the range of 0.932 (for a resolution of \((192\times 192)\)) to 0.967 (for a resolution of \((128\times 128)\)) for the \"Simple\" convolutional embedder and 0.911 (for a resolution of \((96\times 96)\)) to 0.961 (for a resolution of \((128\times 128)\)) for the \"Natural\" convolutional embedder. The use of GradCAM-based feature extraction allowed further improvement of NN approximation results. The GradCAM-based method had the best accuracy of approximation in the range of 0.945 (for a resolution of \((192\times 192)\)) to 0.984 (for a resolution of \((56\times 56)\)) for the \"Simple\" convolutional embedder and 0.922 (for a resolution of \((48\times 48)\)) to 0.968 (for a resolution of \((128\times 128)\)) for the \"Natural\" convolutional embedder. The use of GradCAM resulted in an improvement in NN approximation relative to the Naive method of about 0.01 to 0.02. Most often, our method had the highest approximation values for \(\alpha=1\) and a low value of \(t\in 0.1,0.2,0.3\). This means that to achieve good approximation, there is no need for additional modulation or thresholding of \(M^{c}\) map. Also, no relationship was observed between the resolution of the input signal and the accuracy of approximation. The decision trees that were generated using the method described in Section 2.4 were relatively easy to interpret.