Corpus ID: 231846835

A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management

@article{Huang2021AMA,
  title={A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management},
  author={Zhenhan Huang and F. Tanaka},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.03502}
}
Financial Portfolio Management is one of the most applicable problems in Reinforcement Learning (RL) by its sequential decision-making nature. Existing RL-based approaches, while inspiring, often lack scalability, reusability, or profundity of intake information to accommodate the everchanging capital markets. In this paper, we design and develop MSPM, a novel Multi-agent Reinforcement learning-based system with a modularized and scalable architecture for portfolio management. MSPM involves two… Expand

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References

SHOWING 1-10 OF 28 REFERENCES
MAPS: Multi-Agent reinforcement learning-based Portfolio management System
  • 1
  • Highly Influential
  • PDF
Designing a Multi-Agent Portfolio Management System
  • 44
  • PDF
Adversarial Deep Reinforcement Learning in Portfolio Management
  • 40
Online portfolio selection: A survey
  • 153
  • PDF
Deep Reinforcement Learning with Double Q-Learning
  • 2,558
  • PDF
...
1
2
3
...