August 2022
Intermediate to advanced
394 pages
12h 32m
Chinese
Content preview from 金融人工智能：用Python实现AI量化交易
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203
第 9 章
强化学习
像人类一样，我们的智能体能够自己学习以实现成功的策略，从而获得最大的长
期回报。这种通过完全基于自奖励或惩罚的试错学习的范式被称为强化学习。
1
——
DeepMind
，
2016
年
第
7
章和第
8
章中应用的学习算法属于
监督学习
的范畴，这些方法要求有一个包含特征和
标签的数据集，此数据集能够支持这些算法学习特征和标签之间的关系，从而在估计或
分类任务中取得成功。正如第
1
章中的简单示例所示
，
强化学习
（
reinforcement learning
，
RL
）的工作方式与此不同
。它不需要预先给出一个全面的特征和标签数据集，数据是由
学习智能体在与感兴趣的环境交互时生成的。本章详细介绍了强化学习，说明了其基本概
念，以及该领域最流行的算法之一：
Q
学习
。神经网络不会被强化学习算法取代，在本章
的讨论范畴内它们通常也发挥着重要作用。
9.1
节解释了强化学习中的基本概念
，比如环境、状态和智能体。
9.2
节介绍了强化学习环
境的
OpenAI Gym
套件
，其中是以
CartPole
环境作为示例的。第
2
章简要介绍和讨论过在
这种环境中，智能体必须学习如何通过将推车向左或向右移动来平衡推车上的杆子。
9.3
节
展示了如何使用降维和蒙特卡罗模拟来解决
CartPole
问题。
DNN
等标准监督学习算法通
常不适合解决像
CartPole
这样的问题，因为它们缺乏延迟奖励的概念，
9.4
节会对此进行
说明。
9.5
节讨论了
DQL
智能体
，该智能体明确地考虑了延迟奖励并能够解决
CartPole
问
题。
9.6
节将相同的智能体应用于简单的金融市场环境 ...
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