Abstract: This paper applies DRL(Deep Reinforcement Learning) technology to portfolio management and adopts DDPG (Deep Deterministic Policy Gradient) algorithm. By limiting the weight of individual stock, risk diversification is achieved and by using Dropout, that is, randomly dropping some nodes when training models, over-fitting problems are solved. Taking Chinese stock market as an example, this paper selects 16 of China securities 100 index constituent stocks as risky assets. The experimental results show that the 2-year accumulative return rate of the portfolio constructed in this paper reaches 65%， which is about 2.5 times of that of the control group（a portfolio with weights evenly distributed among the same 16 stocks). This strongly indicates the effectiveness of the method. Moreover, through further experiments, this paper indicates that the closer the data used for training is from the test data, the better the performance of the portfolio constructed in this paper.

Key words: deep reinforcement learning(DRL), deep deterministic policy gradient(DDPG), portfolio management