In the rapidly evolving field of artificial intelligence, chat agents have become integral to customer service, technical support, online sales, and more. These agents solve complex tasks by breaking them down into smaller, manageable sub-tasks, and utilizing tools to retrieve knowledge and perform actions. Additionally, they memorize the context of conversations to make informed decisions.
Machine learning projects frequently require a sequence of preprocessing tasks to prepare the data for model training. Such tasks can range from filling in missing values and normalizing numerical data to encoding categorical data. The scikit-learn library simplifies this process through its Pipeline class. This handy tool allows to bundle the preprocessing steps and model training into one unified workflow, acting much like an individual estimator.
This blog post delves into the topic of Tabular Q-Learning, a specific type of Q-Learning. Q-Learning is used in various applications like game playing, robot navigation, in economics and trade, and many more. It's particularly useful when the problem model is not known i.e., when the outcomes for actions are not predictable.