Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. It is naturally formulated as a sequential decision-making problem as it requires choosing the appropriate investment until a goal is achieved. Consequently, reinforcement learning, a machine learning technique appropriate for sequential decision-making, offers a promising path for optimizing these investment strategies. In this paper, a novel approach for robust goal-based wealth management based on deep reinforcement learning is proposed. The experimental results indicate its superiority over several goal-based wealth management benchmarks on both simulated and historical market data.

翻译：目标导向投资是一种优先考虑实现特定财务目标的财富管理方法。由于其需要在达成目标前选择合适的投资策略，该问题自然被建模为序贯决策问题。因此，适用于序贯决策的机器学习技术——强化学习，为优化这些投资策略提供了可行路径。本文提出了一种基于深度强化学习的稳健目标导向财富管理新方法。实验结果表明，在模拟市场数据与历史市场数据上，该方法均优于多种目标导向财富管理基准模型。