Retirement Portfolio Allocation Using Deep Reinforcement Learning

Codebase: In Progress

Synopsis: We explore retirement portfolio allocation using deep reinforcement learning to develop a model capable of optimizing investment strategies for long-term financial planning. Traditional retirement portfolio management are trained and tested on synthetic data, which often fail to adapt to real-world scenarios and individual goals. By employing deep reinforcement learning along with real household financial data, we aim to create an intelligent agent that learns to balance risk and reward over extended periods, aiming to maximize the portfolio’s return while considering an individual’s risk tolerance and retirement timeline.

Paper: In Progress