Enhancing Portfolio Optimization via Heuristic-Guided Inverse Reinforcement Learning with Multi-Objective Reward and Graph-based Policy Learning
Portfolio optimization encounters persistent challenges in adapting to dynamic markets due to static assumptions and high-dimensional decision spaces. Although reinforcement learning (RL) has emerged as a potential solution, conventional reward engineering often fails to capture complex market dynam