Advances in Personalized Investment Advisory through Reinforcement Learning: A Technical Review

Abstract

Reinforcement learning (RL) represents a transformative technology in personalized investment advisory services, addressing fundamental limitations of traditional static approaches. This article explores the application of diverse RL frameworks to financial decision-making, from contextual multi-armed bandits for tactical allocations to full Markov Decision Processes for long-term planning. The integration of sophisticated state representations, multi-objective reward functions, and offline learning methodologies enables systems that adapt to individual investor behaviors while maintaining appropriate risk controls. Technical implementations demonstrate measurable improvements in risk-adjusted returns, behavioral alignment, and client retention across various market conditions. Key innovations include artificial potential field representations, privacy-preserving federated architectures, uncertainty-aware distributional modeling, and potential-based reward shaping techniques that accelerate learning while preserving optimality guarantees. As these systems evolve, they promise to democratize access to sophisticated financial guidance by reducing minimum viable account sizes while maintaining service quality, extending professional advisory capabilities to broader populations with diverse financial needs.