mlsanityData to 14 Oct 2026

Dueling Deep Reinforcement Learning for Financial Time Series

arXiv 2504.11601 ↗First posted 15 Apr 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

Reinforcement learning (RL) has emerged as a powerful paradigm for solving decision-making problems in dynamic environments. In this research, we explore the application of Double DQN (DDQN) and Dueling Network Architectures, to financial trading tasks using historical SP500 index data. Our focus is training agents capable of optimizing trading strategies while accounting for practical constraints such as transaction costs. The study evaluates the model performance across scenarios with and without commissions, highlighting the impact of cost-sensitive environments on reward dynamics. Despite computational limitations and the inherent complexity of financial time series data, the agent successfully learned meaningful trading policies. The findings confirm that RL agents, even when trained on limited datasets, can outperform random strategies by leveraging advanced architectures such as DDQN and Dueling Networks. However, significant challenges persist, particularly with a sub-optimal policy due to the complexity of data source.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 13 references that are in the map. First posted on arXiv 15 Apr 2025.

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8 of 13 references.

References · 8

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2016On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minimacited by 1,352
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  4. 2017Deep Reinforcement Learning that Matterscited by 754
  5. 2018A Study on Overfitting in Deep Reinforcement Learningcited by 174
  6. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
  7. 2021Recent Advances in Reinforcement Learning in Financecited by 110
  8. 2022Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Networkcited by 2

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