  The autonomous trading agent is one of the most actively studied areas of
artificial intelligence to solve the capital market portfolio management
problem. The two primary goals of the portfolio management problem are
maximizing profit and restrainting risk. However, most approaches to this
problem solely take account of maximizing returns. Therefore, this paper
proposes a deep reinforcement learning based trading agent that can manage the
portfolio considering not only profit maximization but also risk restraint. We
also propose a new target policy to allow the trading agent to learn to prefer
low-risk actions. The new target policy can be reflected in the update by
adjusting the greediness for the optimal action through the hyper parameter.
The proposed trading agent verifies the performance through the data of the
cryptocurrency market. The Cryptocurrency market is the best test-ground for
testing our trading agents because of the huge amount of data accumulated every
minute and the market volatility is extremely large. As a experimental result,
during the test period, our agents achieved a return of 1800% and provided the
least risky investment strategy among the existing methods. And, another
experiment shows that the agent can maintain robust generalized performance
even if market volatility is large or training period is short.
