# MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System

arXiv: https://arxiv.org/abs/2007.05402 · HTML: https://mlsanity.com/paper/2007.05402/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [Automate Strategy Finding with LLM in Quant Investment](https://mlsanity.com/paper/2409.06289/) (2024)
- [A Review of Reinforcement Learning in Financial Applications](https://mlsanity.com/paper/2411.12746/) (2024)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management](https://mlsanity.com/paper/2102.03502/) (2021)
- [MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization](https://mlsanity.com/paper/2210.01774/) (2022)
- [Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2512.23515/) (2025)
- [Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges](https://mlsanity.com/paper/2305.10091/) (2023)
- [Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent](https://mlsanity.com/paper/2407.14486/) (2024)
- [DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization](https://mlsanity.com/paper/2509.12753/) (2025)
- [Assisted Learning for Organizations with Limited Imbalanced Data](https://mlsanity.com/paper/2109.09307/) (2021)
- [Building crypto portfolios with agentic AI](https://mlsanity.com/paper/2507.20468/) (2025)
- [MARS: A Meta-Adaptive Reinforcement Learning Framework for Risk-Aware Multi-Agent Portfolio Management](https://mlsanity.com/paper/2508.01173/) (2025)
- [GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learning](https://mlsanity.com/paper/2606.08450/) (2026)
- [Market-Dependent Communication in Multi-Agent Alpha Generation](https://mlsanity.com/paper/2511.13614/) (2025)
- [Regime-Adaptive Continual Learning for Portfolio Management](https://mlsanity.com/paper/2606.00143/) (2026)

## Abstract (arXiv)

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing return. However, these models often fail to consider and adapt to the continuously changing market conditions. In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS). MAPS is a cooperative system in which each agent is an independent "investor" creating its own portfolio. In the training procedure, each agent is guided to act as diversely as possible while maximizing its own return with a carefully designed loss function. As a result, MAPS as a system ends up with a diversified portfolio. Experiment results with 12 years of US market data show that MAPS outperforms most of the baselines in terms of Sharpe ratio. Furthermore, our results show that adding more agents to our system would allow us to get a higher Sharpe ratio by lowering risk with a more diversified portfolio.