标题： MARS：一种风险感知多智能体投资组合管理的元自适应强化学习框架 显示英文标题

标题： MARS: A Meta-Adaptive Reinforcement Learning Framework for Risk-Aware Multi-Agent Portfolio Management

摘要： 强化学习（RL）在自动化投资组合管理中显示出巨大的潜力；然而，有效平衡风险和回报仍然是一个核心挑战，因为许多模型无法适应动态变化的市场条件。 在本文中，我们提出了面向风险意识系统的元控制代理（MARS），这是一种新颖的RL框架，旨在通过多代理、风险意识的方法明确解决这一限制。 MARS不采用单一的庞大模型，而是使用了一个异构代理集合，其中每个代理具有独特的内在风险特征。 该特征由专门的安全评论网络和特定的风险容忍度阈值强制执行，使代理能够专注于从资本保全到激进增长的行为。 为了应对不同的市场制度，一个高层的元自适应控制器（MAC）学习动态协调该集合。 通过调整其对保守型与激进型代理的依赖程度，MAC在下跌期间有效降低投资组合波动性，并在牛市中寻求更高的回报，从而最小化最大回撤并增强整体稳定性。 这种双层结构使MARS能够生成一个有纪律且适应性强的投资组合，能够抵御市场波动。 该框架通过利用行为多样性而非显式的市场特征工程，实现了风险与回报之间的优越平衡。 在主要国际股票指数上的实验，包括重大金融危机时期，证明了我们的框架在风险调整标准上的有效性，显著减少了最大回撤和波动性，同时保持了有竞争力的回报。 显示英文摘要

摘要： 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. In this paper, we propose Meta-controlled Agents for a Risk-aware System (MARS), a novel RL framework designed to explicitly address this limitation through a multi-agent, risk-aware approach. Instead of a single monolithic model, MARS employs a Heterogeneous Agent Ensemble where each agent possesses a unique, intrinsic risk profile. This profile is enforced by a dedicated Safety-Critic network and a specific risk-tolerance threshold, allowing agents to specialize in behaviors ranging from capital preservation to aggressive growth. To navigate different market regimes, a high-level Meta-Adaptive Controller (MAC) learns to dynamically orchestrate the ensemble. By adjusting its reliance on conservative versus aggressive agents, the MAC effectively lowers portfolio volatility during downturns and seeks higher returns in bull markets, thus minimizing maximum drawdown and enhancing overall stability. This two-tiered structure allows MARS to generate a disciplined and adaptive portfolio that is robust to market fluctuations. The framework achieves a superior balance between risk and return by leveraging behavioral diversity rather than explicit market-feature engineering. Experiments on major international stock indexes, including periods of significant financial crisis, demonstrate the efficacy of our framework on risk-adjusted criteria, significantly reducing maximum drawdown and volatility while maintaining competitive returns.