论文标题
结合加强学习和资产分配建议的逆增强学习
Combining Reinforcement Learning and Inverse Reinforcement Learning for Asset Allocation Recommendations
论文作者
论文摘要
我们建议一种简单的实用方法，将人工和人工智能结合起来，以学习基金经理的最佳投资实践，并提供建议以改善它们。我们的方法是基于反钢筋学习（IRL）和RL的组合。首先，IRL组件按照其交易历史记录所建议的基金经理的意图，并恢复其隐含的奖励功能。在第二步中，直接RL算法使用此奖励功能来优化资产分配决策。我们表明，我们的方法能够改善个人基金经理的绩效。
We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers.
