AI RESEARCH
Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies
arXiv CS.LG
•
ArXi:2502.17518v2 Announce Type: replace This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging classifier models to enhance performance. By combining RL algorithms such as A2C, PPO, and SAC with traditional classifiers like Vector Machines (SVM), Decision Trees, and Logistic Regression, we investigate how different classifier groups can be integrated to improve risk-return trade-offs.