Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies

This paper introduces an ensemble approach to Reinforcement Learning (RL) in financial trading, integrating RL algorithms like A2C, PPO, and SAC with traditional classifiers such as SVM, Decision Trees, and Logistic Regression. The study demonstrates that these ensemble methods can enhance risk-return trade-offs, outperforming individual models in metrics like Cumulative Returns and Sharpe Ratios, although their effectiveness is contingent on factors such as variance threshold and market conditions. This research highlights the potential for improved decision-making in dynamic environments by combining RL with classifiers, offering valuable insights for practitioners in finance and robotics.