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.
@article{arxiv.2402.03338,
title = {CNN-DRL with Shuffled Features in Finance},
author = {Sina Montazeri and Akram Mirzaeinia and Amir Mirzaeinia},
journal= {arXiv preprint arXiv:2402.03338},
year = {2024}
}
Comments
10th Annual Conf. on Computational Science & Computational Intelligence (CSCI'23). arXiv admin note: substantial text overlap with arXiv:2401.06179