CNN-DRL with Shuffled Features in Finance
Sina Montazeri Akram Mirzaeinia Amir Mirzaeinia
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.