How Heuristic‑Guided Inverse Reinforcement Learning Boosts Portfolio Optimization
The article presents a heuristic‑guided inverse reinforcement learning framework that generates expert strategies respecting industry diversification and correlation constraints, employs a multi‑objective reward to balance return and risk, and uses a heterogeneous graph attention network to model stock relationships, achieving superior risk‑adjusted returns on CSI‑300, CSI‑500, NASDAQ‑100 and S&P‑500 benchmarks.
