# CNN-DRL with Shuffled Features in Finance

arXiv: https://arxiv.org/abs/2402.03338 · HTML: https://mlsanity.com/paper/2402.03338/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets](https://mlsanity.com/paper/2110.14771/) (2021)
- [Towards Realistic Market Simulations: a Generative Adversarial Networks Approach](https://mlsanity.com/paper/2110.13287/) (2021)
- [Towards a fully RL-based Market Simulator](https://mlsanity.com/paper/2110.06829/) (2021)
- [FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance](https://mlsanity.com/paper/2111.05188/) (2021)

## Built on by (most cited first, 3 of 3)

- [Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity](https://mlsanity.com/paper/2407.09557/) (2024)
- [Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learning](https://mlsanity.com/paper/2408.11859/) (2024)
- [Finding Optimal Trading History in Reinforcement Learning for Stock Market Trading](https://mlsanity.com/paper/2502.12537/) (2025)

## Abstract (arXiv)

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.