mlsanityData to 7 Oct 2026

CNN-DRL with Shuffled Features in Finance

arXiv 2402.03338 ↗First posted 16 Jan 2024q-fin.CP cs.LGcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 16 Jan 2024.

Builds on

7 of 11 references.

References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2019Deep Reinforcement Learning for Tradingcited by 73
  3. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  4. 2021ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Marketscited by 34
  5. 2021Towards Realistic Market Simulations: a Generative Adversarial Networks Approachcited by 29
  6. 2021Towards a fully RL-based Market Simulatorcited by 10
  7. 2021FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Financecited by 9

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversitycited by 2
  2. 2024Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learningcited by 1
  3. 2025Finding Optimal Trading History in Reinforcement Learning for Stock Market Tradingcited by 1