MARS用多智能体动态调节风险，让投资组合更稳更抗跌。
MARS: A Meta-Adaptive Reinforcement Learning Framework for Risk-Aware Multi-Agent Portfolio Management
- 用不同风险偏好的智能体组成团队，由上级控制器实时调配。
- 在国际指数上测试，最大回撤和波动率显著降低，收益仍具竞争力。
- 适合追求稳健收益、需应对市场突变的量化投资者。
强化学习在自动化投资组合管理中展现出巨大潜力；然而，有效平衡风险与回报仍是核心挑战，许多模型无法适应动态变化的市场环境。我们提出元控制风险感知系统（MARS），一种基于多智能体的风险感知框架。MARS以异构智能体集成取代单一模型，每个智能体通过安全评判网络（Safety-Critic）强制执行独特风险偏好，行为范围从保守保本到激进增长。高层元自适应控制器（MAC）动态调度该集成，在市场下跌时减少高风险依赖，上涨时把握机会。这种双层结构利用行为多样性而非显式特征工程，确保投资组合在不同市场环境下保持稳健。在主要国际指数上的实验表明，该框架显著降低最大回撤和波动率，同时维持有竞争力的收益。
原文摘要 · Abstract (English)
Reinforcement Learning (RL) has shown significant promise in automated portfolio management; however, effectively balancing risk and return remains a central challenge, as many models fail to adapt to dynamically changing market conditions. We propose Meta-controlled Agents for a Risk-aware System (MARS), a novel framework addressing this through a multi-agent, risk-aware approach. MARS replaces monolithic models with a Heterogeneous Agent Ensemble, where each agent's unique risk profile is enforced by a Safety-Critic network to span behaviors from capital preservation to aggressive growth. A high-level Meta-Adaptive Controller (MAC) dynamically orchestrates this ensemble, shifting reliance between conservative and aggressive agents to minimize drawdown during downturns while seizing opportunities in bull markets. This two-tiered structure leverages behavioral diversity rather than explicit feature engineering to ensure a disciplined portfolio robust across market regimes. Experiments on major international indexes confirm that our framework significantly reduces maximum drawdown and volatility while maintaining competitive returns.
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