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Background <p>Automated equity portfolio management is an important challenge in computational finance where the conventional optimization approaches fail to cope with non-stationary market forces and multifaceted decision-making processes. Deep reinforcement learning (DRL) algorithms have become a natural paradigm of sequential portfolio allocation decisions, providing their adaptive learning ability potentially more successful than traditional methods.</p> Objective <p>This review will look at how automated portfolio manAdvanced Actoragement systems based on DRL have developed over time, beginning with single-agent applications, to more complex multi-agent ensemble systems. Our analysis of algorithmic advances, system architecture styles and technical implementation plans that have influenced this domain between 2018 and 2025.</p> Methods <p>We have done a full search based on PRISMA practice by utilizing various scholarly databases, such as IEEE Xplore, ACM Digital Library, arXiv and Google Scholar. We used a search strategy based on deep reinforcement learning algorithms used in equity portfolio management with particular attention to technical applications and systems design. We obtained descriptive algorithm specifications, network structures, training procedures and performance indicators in 156 peer-reviewed articles.</p> Results <p>The discussion shows that there were four different evolutionary phases, with Phase I (2018–2020) consisting of simple single-agent models such as Deep Q-Networks (DQN) and REINFORCE with a low Sharpe ratio (0.7–1.0). In Phase II (2020–2022), improved phase II policies overcame phase I (2015–2017) results due to the implementation of novel advanced phase II policy gradient methods such as Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), and Soft Actor-Critic (SAC) with Sharpe ratios of 1.2–1.4. The phase III (2022–2024) was the stage of development of multi-agent systems with cooperative, competitive, and hierarchical structures with a Sharpe ratio of 1.5–1.8. Phase IV (2024–2025) is the next horizon where the ensemble systems will incorporate a number of DRL algorithms and exhibit a Sharpe ratio of 1.8–2.4 and accrue a maximum drawdown of about 8%.</p> Conclusion <p>The systematic development of single-agent to multi-agent ensemble systems shows great improvements in algorithmic and architecture of DRL-based portfolio management. The identification of the essential system design patterns, algorithmic developmental courses and technical implementation issues are some of the contributions. The research in the future focuses on the importance of explainable AI integration, meta-learning market regime adaptation, and consistent evaluation systems in reproducible research.</p>

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Systematic review of reinforcement learning for automated equity portfolio management from single agent to multi agent systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

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  • Aditi Kumar Rout,
  • U. Dinesh Acharya,
  • Prakash K. Aithal,
  • Arpita Sharma

摘要

Background

Automated equity portfolio management is an important challenge in computational finance where the conventional optimization approaches fail to cope with non-stationary market forces and multifaceted decision-making processes. Deep reinforcement learning (DRL) algorithms have become a natural paradigm of sequential portfolio allocation decisions, providing their adaptive learning ability potentially more successful than traditional methods.

Objective

This review will look at how automated portfolio manAdvanced Actoragement systems based on DRL have developed over time, beginning with single-agent applications, to more complex multi-agent ensemble systems. Our analysis of algorithmic advances, system architecture styles and technical implementation plans that have influenced this domain between 2018 and 2025.

Methods

We have done a full search based on PRISMA practice by utilizing various scholarly databases, such as IEEE Xplore, ACM Digital Library, arXiv and Google Scholar. We used a search strategy based on deep reinforcement learning algorithms used in equity portfolio management with particular attention to technical applications and systems design. We obtained descriptive algorithm specifications, network structures, training procedures and performance indicators in 156 peer-reviewed articles.

Results

The discussion shows that there were four different evolutionary phases, with Phase I (2018–2020) consisting of simple single-agent models such as Deep Q-Networks (DQN) and REINFORCE with a low Sharpe ratio (0.7–1.0). In Phase II (2020–2022), improved phase II policies overcame phase I (2015–2017) results due to the implementation of novel advanced phase II policy gradient methods such as Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), and Soft Actor-Critic (SAC) with Sharpe ratios of 1.2–1.4. The phase III (2022–2024) was the stage of development of multi-agent systems with cooperative, competitive, and hierarchical structures with a Sharpe ratio of 1.5–1.8. Phase IV (2024–2025) is the next horizon where the ensemble systems will incorporate a number of DRL algorithms and exhibit a Sharpe ratio of 1.8–2.4 and accrue a maximum drawdown of about 8%.

Conclusion

The systematic development of single-agent to multi-agent ensemble systems shows great improvements in algorithmic and architecture of DRL-based portfolio management. The identification of the essential system design patterns, algorithmic developmental courses and technical implementation issues are some of the contributions. The research in the future focuses on the importance of explainable AI integration, meta-learning market regime adaptation, and consistent evaluation systems in reproducible research.