Statistics is a branch of mathematics that extracts useful information from data. The data is generally called as "training data" in statistical (machine) learning.
Consider the following paragraph from the
section 1.1 Reinforcement Learning of
CHAPTER 1. THE REINFORCEMENT LEARNING PROBLEM of the textbook Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.
Reinforcement learning is different from supervised learning, the kind of learning studied in most current research in field of machine learning. Supervised learning is learning from a training set of labeled examples provided by a knowledgable external supervisor. Each example is a description of a situation together with a specification—the label—of the correct action the system should take to that situation, which is often to identify a category to which the situation belongs. The object of this kind of learning is for the system to extrapolate, or generalize, its responses so that it acts correctly in situations not present in the training set. This is an important kind of learning, but alone it is not adequate for learning from interaction. In interactive problems it is often impractical to obtain examples of desired behavior that are both correct and representative of all the situations in which the agent has to act. In uncharted territory—where one would expect learning to be most beneficial—an agent must be able to learn from its own experience.
You can observe that, training data in machine learning, if we model it in a proper format, can be for reinforcement learning. But, may not be complete and practical.
I am asking this question in the view of statistical learning rather than machine learning alone. training data in statistical learning can be understood to any data, at any time instant, useful in learning.
Then, is it perfectly fine to always interpret experience in reinforcement learning as training data in statistical learning?