Augmented Lagrangian Risk-Constrained Reinforcement Learning for Portfolio Optimization (Student Abstract)
Abstract
We applied Risk-averse Reinforcement Learning (RL) to optimize investment portfolios while incorporating risk constraints. Given that portfolios must adhere to risk constraints set by investors and regulators, enforcing hard constraints is essential for practical portfolio optimization. Traditional techniques often lack the flexibility to model the complexities of dynamic financial markets. To address this, we used the Augmented Lagrangian Multiplier (ALM) to impose constraints on the agent, reducing risk during decision-making. Our risk-constrained RL algorithm demonstrated no constraint violations during testing and outperformed other Risk-averse RL methods, indicating its potential for optimizing portfolios for risk-averse investors.
Cite
Text
Enkhsaikhan and Jo. "Augmented Lagrangian Risk-Constrained Reinforcement Learning for Portfolio Optimization (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35252Markdown
[Enkhsaikhan and Jo. "Augmented Lagrangian Risk-Constrained Reinforcement Learning for Portfolio Optimization (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/enkhsaikhan2025aaai-augmented/) doi:10.1609/AAAI.V39I28.35252BibTeX
@inproceedings{enkhsaikhan2025aaai-augmented,
title = {{Augmented Lagrangian Risk-Constrained Reinforcement Learning for Portfolio Optimization (Student Abstract)}},
author = {Enkhsaikhan, Bayaraa and Jo, Ohyun},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {29362-29364},
doi = {10.1609/AAAI.V39I28.35252},
url = {https://mlanthology.org/aaai/2025/enkhsaikhan2025aaai-augmented/}
}