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[2J4-GS-2-03] Compound deep reinforcement learning to acquire trading strategies in a complex environment
Keywords:Reinforcement learning, finance
Previous research used only two state variables while one of the good points of deep reinforcement learning is that it can use many state variables.
And it used a compound deep reinforcement learning algorithm based on the simplified DQN that is the earliest deep reinforcement learning algorithm.
In this paper, we extend the latest algorithms of deep reinforcement learning to a compound type and acquire a financial trading strategy in a complex environment represented by many state variables.
In addition, we applied the proposed method to acquire a trading strategy for Japanese government bonds and confirmed its effectiveness.
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