A Learning Agent is a tool that is capable of learning from its experiences. With the explosion of research and applications for Artificial Intelligence, a need has developed for methods that provide insights into what could be happening inside the machine/deep learning black boxes, called explainability. A number of researchers have turned to information theory to explore the processes by which a learning agent acquires knowledge from large data sets and utilizes this knowledge in A.I. applications. More specifically, some researchers have backed away from trying to understand what is happening inside the machine/deep learning black boxes and considered what information is available to be learned from sequences with various properties. So, the focus is on using a learning agent to analyze time series sequences of data [4, 6, 15, 16, 18, 19, 21, 22, 38, 38, 43]. Information theoretic principles play a large role in these analyses.