论文标题
深层确定性投资组合优化
Deep Deterministic Portfolio Optimization
论文作者
论文摘要
深度强化学习算法是否可以用作最佳交易策略的解决者？这项工作的目的是在概念上简单但数学上的非平凡的交易环境中测试增强算法。选择环境，以使最佳或近距离交易策略已知。我们研究了深层的确定性政策梯度算法，并表明这种强化学习代理可以成功恢复最佳交易策略的基本特征并获得最接近的奖励。
Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an optimal or close-to-optimal trading strategy is known. We study the deep deterministic policy gradient algorithm and show that such a reinforcement learning agent can successfully recover the essential features of the optimal trading strategies and achieve close-to-optimal rewards.
