CNN-DRL with Shuffled Features in Finance
<|reference_start|>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.<|reference_end|>
@article{montazeri2024cnn-drl,
    title={CNN-DRL with Shuffled Features in Finance},
    author={Sina Montazeri, Akram Mirzaeinia, Amir Mirzaeinia},
    journal={arXiv preprint arXiv:2402.03338},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03338},
    primaryClass={q-fin.CP cs.LG}
}