This work proposes a novel portfolio management method using deep reinforcement learning on markets with a dynamic number of assets. This problem is especially important in cryptocurrency markets, which already support the trading of hundreds of assets with new ones being added every month. A novel neural network architecture is proposed, which is trained using deep reinforcement learning. Our architecture considers all assets in the market, and automatically adapts when new ones are suddenly introduced, making our method more general and sample-efficient than previous methods. Further, transaction cost minimization is considered when formulating the problem. For this purpose, a novel algorithm to compute optimal transactions given a desired portfolio is integrated into the architecture. The proposed method was tested on a dataset of one of the largest cryptocurrency markets in the world, outperforming state-of-the-art methods, achieving average daily returns of over 24%.

这项工作提出了一种新颖的投资组合管理方法，该方法在具有动态资产数量的市场上使用深度强化学习。这个问题在加密货币市场中尤其重要，该市场已经支持数百种资产的交易，并且每月都会增加新的资产。提出了一种新颖的神经网络架构，该架构使用深度强化学习进行训练。我们的体系结构考虑了市场上的所有资产，并在突然引入新资产时自动进行调整，从而使我们的方法比以前的方法更具通用性和采样效率。此外，在提出问题时考虑最小化交易成本。为此，将给定所需投资组合的用于计算最佳交易的新颖算法集成到体系结构中。