Multi-Agent Deep Reinforcement Learning with Dynamic Portfolio Weighting: A Novel Approach to Algorithmic Trading

This paper presents a novel ensemble reinforcement learning framework for multi-asset portfolio management, referred to as the Confidence-Weighted Dynamic Ensemble (CWDE). The proposed model integrates five state-of-the-art actor–critic algorithms—PPO, A2C, DDPG, TD3, and SAC—under a dynamic aggregation mechanism that adjusts model weights based on entropy-derived confidence and historical performance. Using a diversified dataset spanning equities, bonds, commodities, and real estate ETFs, CWDE is benchmarked against its constituent DRL agents. Experimental results demonstrate that CWDE outperforms all baselines, achieving the highest risk-adjusted returns and the lowest drawdowns. Statistical analysis confirms the ensemble’s robustness and adaptability to market volatility. The findings highlight CWDE’s potential to serve as a scalable and interpretable framework for trading intelligence. The study concludes with a discussion of computational and practical limitations and outlines future directions for integrating explainability, macroeconomic features, and real-time deployment.