Multi-Agent Reinforcement Learning Correctable Strategy: A Framework with Correctable Strategies for Portfolio Management
Kuang-Da Wang, Pei-Xuan Li, Hsun-Ping Hsieh, Wen-Chih Peng · 2026
Portfolio management (PM) is a broad investment strategy aimed at risk mitigation through diversified financial product investments.Acknowledging the significance of dynamic adjustments after establishing a portfolio to enhance stability and returns, we propose employing reinforcement learning (RL) to address dynamic decision-making challenges.However, traditional RL methods often struggle to adapt to significant market volatility, primarily by focusing on adjusting existing asset weights.Different from traditional RL methods, the multi-agent reinforcement learning correctable strategy (MAC) developed in this study detects and replaces potentially harmful assets with familiar alternatives, ensuring a resilient response to market crises.Utilizing the multi-agent reinforcement learning model, MAC empowers individual agents to maximize portfolio returns and minimize risk separately.During training, MAC strategically replaces assets to simulate market changes, allowing agents to learn risk-identification through uncertainty estimation.During testing, MAC detects potentially harmful assets and replaces them with more reliable alternatives, enhancing portfolio stability.Experiments conducted on a real-world US Exchange-Traded Fund (ETF) market dataset demonstrate MAC's superiority over standard RL-based PM methods and other baselines, underscoring its practical efficacy for real-world applications.