Deep Reinforcement learning is a branch of unsupervised learning in which an agent learns to act based on environment state in order to maximize its total reward. Deep reinforcement learning provides good opportunity to model the complexity of portfolio choice in high-dimensional and data-driven environment by leveraging the powerful representation of deep neural networks. In this paper, we build a portfolio management system using direct deep reinforcement learning to make optimal portfolio choice periodically among S\&P500 underlying stocks by learning a good factor representation (as input). The result shows that an effective learning of market conditions and optimal portfolio allocations can significantly outperform the average market.

翻译：深度强化学习是不受监督的学习分支,其中,代理人学会根据环境状况采取行动,以最大限度地获得全部回报。深层强化学习通过利用深层神经网络的强大代表性,为在高度和数据驱动的环境中模拟组合选择的复杂性提供了良好机会。在本文中,我们建立了一个组合管理系统,利用直接强化学习,通过学习良好的要素代表(作为投入),定期在S ⁇ P500基础种群中做出最佳组合选择。结果显示,有效学习市场条件和最佳组合分配能够大大超过平均市场。