CNN-DRL with Shuffled Features in Finance

Sina Montazeri, Akram Mirzaeinia and Amir Mirzaeinia

Papers from arXiv.org

Abstract: 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.

Date: 2024-01
New Economics Papers: this item is included in nep-big and nep-cmp
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