CNN-DRL with Shuffled Features in Finance

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.

agent application cnn convolutional neural networks cs.lg data feature features finance financial matrix networks neural networks prior q-fin.cp reinforcement reinforcement learning study vector