  In prior methods, it was observed that the application of Convolutional
Neural Networks agent in Deep Reinforcement Learning to financial data resulted
in an enhanced reward. In this study, a specific permutation was applied to the
feature vector, thereby generating a CNN matrix that strategically positions
more pertinent features in close proximity. Our comprehensive experimental
evaluations unequivocally demonstrate a substantial enhancement in reward
attainment.
