  Investment portfolios, central to finance, balance potential returns and
risks. This paper introduces a hybrid approach combining Markowitz's portfolio
theory with reinforcement learning, utilizing knowledge distillation for
training agents. In particular, our proposed method, called KDD (Knowledge
Distillation DDPG), consist of two training stages: supervised and
reinforcement learning stages. The trained agents optimize portfolio assembly.
A comparative analysis against standard financial models and AI frameworks,
using metrics like returns, the Sharpe ratio, and nine evaluation indices,
reveals our model's superiority. It notably achieves the highest yield and
Sharpe ratio of 2.03, ensuring top profitability with the lowest risk in
comparable return scenarios.
