  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.
