论文标题
离线的深入强化学习，用于动态定价消费者信用
Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit
论文作者
论文摘要
我们介绍了一种使用脱机深入强化学习的最新进展来定价消费者信用的方法。这种方法依赖于静态数据集，并且不需要对需求的功能形式的假设。使用有关消费者信用应用程序的真实和合成数据，我们证明了我们使用保守Q学习算法的方法能够学习有效的个性化定价策略，而无需任何在线互动或价格实验。
We introduce a method for pricing consumer credit using recent advances in offline deep reinforcement learning. This approach relies on a static dataset and requires no assumptions on the functional form of demand. Using both real and synthetic data on consumer credit applications, we demonstrate that our approach using the conservative Q-Learning algorithm is capable of learning an effective personalized pricing policy without any online interaction or price experimentation.
