In this work, we propose a method to explain the performance of a neural network-based brain of a machine learning agent trained with a Proximal Policy Optimization (PPO) algorithm. By explaining, we mean to discover probable criteria on which the PPO-trained agent system is making certain decisions. An additional complication is that the agent retrieves information about the environment using a camera. Our proposed explanation method consists of Gradient-weighted Class Activation Mapping (GradCAM) and Learning from Examples using Rough Sets (LEM2) algorithm. We also show how using a threshold on rules support and Laplace, we can reduce the number of rules and measure how reducing the number of rules affects the accuracy of approximation of the explained model. To the best of our knowledge, this is the first work proposing a PPO-generated network explanation for visual-based agents using the rough sets-based method. Our method has achieved a high efficiency of explanation of neural network performance on the test set, reaching F1 score = 0.93 and an Average per-class accuracy of 0.94. In some cases, we have also reduced the number of rules to a few or a dozen while maintaining a high level of explainability efficiency. We publish all source codes so our experiments can be reproduced.