Preference Learning and Deep Reinforcement Learning (TD3) for Multi‑Manager Portfolio Strategy Selection

Author(s): Shenggang Li

Originally published on Towards AI.

From Human Manager Trajectories to a Unified Adaptive Allocation Policy via AHP‑Guided Preference Modeling and Actor–Critic Optimization

Traditional asset allocation faces challenges in balancing multiple objectives:

This article addresses the complexities of asset allocation by proposing a model that learns from the decision-making behaviors of experienced portfolio managers. Utilizing AHP-guided modeling and reinforcement learning, the approach emphasizes adapting allocation strategies in real-time while respecting risk parameters and optimizing performance. The integration of preference learning reveals insights into the subtle trade-offs between various risk-adjusted outcomes, ultimately enabling a more robust and automated framework for portfolio management.

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Published via Towards AI