用强化学习微调时间序列预测模型，提升金融预测性能。
Fine-tuning Timeseries Predictors Using Reinforcement Learning
- 将强化学习损失反向传播至监督学习模型，实现微调。
- 微调后模型性能提升，具备迁移学习特性。
- 提供可复现的调参流程与实证结果，适合从业者参考。
本章介绍了三种用于微调金融预测模型的强化学习算法。我们提出了一种清晰的实现方案，将强化学习任务的损失反向传播至通过监督学习训练的模型，并对比了微调前后的性能表现。实验结果显示，微调后模型性能显著提升，且表现出良好的迁移学习特性，验证了微调的有效性。同时，本文还详细阐述了调参过程与实证结果，为实践者未来应用提供参考。
原文摘要 · Abstract (English)
This chapter presents three major reinforcement learning algorithms used for fine-tuning financial forecasters. We propose a clear implementation plan for backpropagating the loss of a reinforcement learning task to a model trained using supervised learning, and compare the performance before and after the fine-tuning. We find an increase in performance after fine-tuning, and transfer learning properties to the models, indicating the benefits of fine-tuning. We also highlight the tuning process and empirical results for future implementation by practitioners.
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