在这本书中，我们探索了一种从交互中学习的计算方法。我们不直接对人或动物如何学习进行理论分析，而是探索理想化的学习情境，评估各种学习方法的效率。也就是说，我们采用人工智能研究人员或工程师的角度。我们探索去设计在这些方面上格外有效率的机器，他能够解决科学或经济学领域的问题。通过数据分析和计算实验来评估这些设计。我们将这种方法称为强化学习，更侧重于目标导向的交互学习，而不是其他方法。(In this book, we explored a computational method of learning from interaction. We do not directly analyze how people or animals learn, but explore idealized learning situations and evaluate the efficiency of various learning methods. That is to say, we adopt the angle of artificial intelligence researchers or engineers. We are exploring ways to design machines that are exceptionally efficient in these areas and that can solve problems in science or economics. These designs are evaluated through data analysis and computational experiments. We call this method reinforcement learning, which focuses more on goal-oriented interactive learning than other methods.)