Reinforcement Learning for Portfolio Optimization: Advancing Intelligent Investment Strategies

Abstract

The application of reinforcement learning (RL) in portfolio optimization has emerged as a powerful approach for enhancing investment decision-making. Unlike traditional optimization models, RL adapts dynamically to market fluctuations by continuously learning from interactions with financial environments. This paper investigates the use of deep reinforcement learning algorithms, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), for constructing robust and diversified investment portfolios. Through simulations on historical market data, RL-based models demonstrate superior risk-adjusted returns compared to conventional strategies such as mean-variance optimization. The study further explores challenges related to transaction costs, overfitting, and model interpretability, offering potential solutions through hybrid approaches. Findings suggest that RL has significant potential in revolutionizing asset allocation, making investment strategies more adaptive, resilient, and efficient in volatile financial markets.