Integrating Large Language Models with Deep Reinforcement Learning for Portfolio Optimization

This paper explores the application of Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) to portfolio optimization, a critical financial task requiring strategies to balance risk and return in volatile markets. Traditional models often struggle with the complexity of financial markets, whereas Reinforcement Learning (RL) provides end-to-end frameworks for learning optimal, dynamic trading policies through sequential decision-making and trial-and-error interactions. The study examines key DRL algorithms, including Q-learning, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Twin-Delayed Deep Deterministic Policy Gradient (TD3), emphasizing their strengths in dynamic asset allocation. Crucial components of financial RL systems are discussed, such as state representations, reward function designs, its algorithms, and main approaches. Furthermore, the survey investigates how LLMs enhance decision-making by analyzing unstructured data (like news and social media) for sentiment and risk assessment, often integrating these insights to augment state representations or guide reward shaping within DRL frameworks.

@article{Alsweed2025,
  title     = {Integrating Large Language Models with Deep Reinforcement Learning for Portfolio Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Renad Alsweed and Mohammed Alsuhaibani},
  doi       = {10.14569/IJACSA.2025.0161184},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161184}
}