Deep reinforcement learning is gaining popularity in many different fields. An interesting sector is related to the definition of dynamic decision-making systems. A possible example is dynamic portfolio optimization, where an agent has to continuously reallocate an amount of fund into a number of different financial assets with the final goal of maximizing return and minimizing risk. In this work, a novel deep Q-learning portfolio management framework is proposed. The framework is composed by two elements: a set of local agents that learn assets behaviours and a global agent that describes the global reward function. The framework is tested on a crypto portfolio composed by four cryptocurrencies. Based on our results, the deep reinforcement portfolio management framework has proven to be a promising approach for dynamic portfolio optimization.

深度强化学习在许多不同领域中越来越受欢迎。一个有趣的领域与动态决策系统的定义有关。一个可能的例子是动态投资组合优化，其中代理商必须不断地将一定数量的资金重新分配到许多不同的金融资产中，最终目标是使回报最大化和风险最小化。在这项工作中，提出了一种新颖的深度Q学习投资组合管理框架。该框架由两个元素组成：一组了解资产行为的本地代理和一个描述全局奖励功能的全局代理。该框架在由四种加密货币组成的加密资产组合上进行了测试。根据我们的结果，深度强化的项目组合管理框架已被证明是用于动态项目组合优化的有前途的方法。