Deep or reinforcement learning (RL) approaches have been adapted as reactive
agents to quickly learn and respond with new investment strategies for
portfolio management under the highly turbulent financial market environments
in recent years. In many cases, due to the very complex correlations among
various financial sectors, and the fluctuating trends in different financial
markets, a deep or reinforcement learning based agent can be biased in
maximising the total returns of the newly formulated investment portfolio while
neglecting its potential risks under the turmoil of various market conditions
in the global or regional sectors. Accordingly, a multi-agent and self-adaptive
framework namely the MASA is proposed in which a sophisticated multi-agent
reinforcement learning (RL) approach is adopted through two cooperating and
reactive agents to carefully and dynamically balance the trade-off between the
overall portfolio returns and their potential risks. Besides, a very flexible
and proactive agent as the market observer is integrated into the MASA
framework to provide some additional information on the estimated market trends
as valuable feedbacks for multi-agent RL approach to quickly adapt to the
ever-changing market conditions. The obtained empirical results clearly reveal
the potential strengths of our proposed MASA framework based on the multi-agent
RL approach against many well-known RL-based approaches on the challenging data
sets of the CSI 300, Dow Jones Industrial Average and S&P 500 indexes over the
past 10 years. More importantly, our proposed MASA framework shed lights on
many possible directions for future investigation.

在高度动荡的金融市场环境下，本研究提出了一个自适应的多智能体框架（MASA），采用了深度学习和强化学习方法作为反应性代理，平衡投资组合的回报和潜在风险。该框架中的市场观察者代理提供了有价值的市场趋势信息，以帮助多智能体反应性学习方法快速适应不断变化的市场条件。经验证实，MASA 框架在过去 10 年的 CSI 300 指数、道琼斯工业平均指数和标准普尔 500 指数上相比其他已知基于强化学习方法的方法表现出潜在优势，并为未来的研究提供了多个可能的方向。

基于深度强化学习的多智能体和自适应框架开发动态投资组合风险管理

Developing A Multi-Agent and Self-Adaptive Framework with Deep  Reinforcement Learning for Dynamic Portfolio Risk Management

ASP programs are a convenient tool for problem solving, whereas with large
problem instances the size of the state space can be prohibitive. We consider
abstraction as a means of over-approximation and introduce a method to
automatically abstract (possibly non-ground) ASP programs that preserves their
structure, while reducing the size of the problem. One particular application
case is the problem of defining declarative policies for reactive agents and
reasoning about them, which we illustrate on examples.

介绍了一种保留 ASP 程序结构的抽象方法，可以自动抽象大型问题空间，并可应用于定义响应性代理的声明性策略及其分析。