  In our paper, we apply deep reinforcement learning approach to optimize
investment decisions in portfolio management. We make several innovations, such
as adding short mechanism and designing an arbitrage mechanism, and applied our
model to make decision optimization for several randomly selected portfolios.
The experimental results show that our model is able to optimize investment
decisions and has the ability to obtain excess return in stock market, and the
optimized agent maintains the asset weights at fixed value throughout the
trading periods and trades at a very low transaction cost rate. In addition, we
redesigned the formula for calculating portfolio asset weights in continuous
trading process which can make leverage trading, that fills the theoretical gap
in the calculation of portfolio weights when shorting.
