  This scientific paper propose a novel portfolio optimization model using an
improved deep reinforcement learning algorithm. The objective function of the
optimization model is the weighted sum of the expectation and value at
risk(VaR) of portfolio cumulative return. The proposed algorithm is based on
actor-critic architecture, in which the main task of critical network is to
learn the distribution of portfolio cumulative return using quantile
regression, and actor network outputs the optimal portfolio weight by
maximizing the objective function mentioned above. Meanwhile, we exploit a
linear transformation function to realize asset short selling. Finally, A
multi-process method is used, called Ape-x, to accelerate the speed of deep
reinforcement learning training. To validate our proposed approach, we conduct
backtesting for two representative portfolios and observe that the proposed
model in this work is superior to the benchmark strategies.
