Reinforcement Learning for Asset and Portfolio Management

This article provides an educational overview of reinforcement learning (RL) and its applications to asset and portfolio management. RL differs from classical optimization by focusing on policies that adapt over time, making it well suited for problems with sequential decisions, frictions, and feedback effects. The authors review applications in trading, hedging, portfolio allocation, and investor preference inference, highlighting both the potential and the challenges of RL. The discussion emphasizes not only where RL has delivered proof-of-concept results but also what portfolio managers and traders should take away for practice. They conclude with practitioner recommendations and an outlook on future directions—including hybrid approaches that combine RL with classical models—the role of richer data and simulators, and the prospects for continual learning in finance.