# CNN-DRL for Scalable Actions in Finance

arXiv: https://arxiv.org/abs/2401.06179 · HTML: https://mlsanity.com/paper/2401.06179/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Cryptocurrency Portfolio Management with Deep Reinforcement Learning](https://mlsanity.com/paper/1612.01277/) (2016)
- [Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review](https://mlsanity.com/paper/2106.00123/) (2021)
- [CNNPred: CNN-based stock market prediction using several data sources](https://mlsanity.com/paper/1810.08923/) (2018)

## Built on by (most cited first, 2 of 2)

- [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)

The published MLP-based DRL in finance has difficulties in learning the dynamics of the environment when the action scale increases. If the buying and selling increase to one thousand shares, the MLP agent will not be able to effectively adapt to the environment. To address this, we designed a CNN agent that concatenates the data from the last ninety days of the daily feature vector to create the CNN input matrix. Our extensive experiments demonstrate that the MLP-based agent experiences a loss corresponding to the initial environment setup, while our designed CNN remains stable, effectively learns the environment, and leads to an increase in rewards.