Evidence, methods, and operating insight for decisions that must work in the field.
An ExaWin+ decision case that updates field observations and customer responses as Bayesian evidence, tracks win probability over 60 days, and allocates limited sales resources to better customers and actions.
I chose Reinforcement Learning (RL) as the first topic of this blog because of my personal interest in a field that has developed from classical theory along a different trajectory from rapidly advancing deep learning and LLM AI agents, while continuing to influence the real world, and because reinforcement learning is also being integrated into Exa-Solution, Exavion’s extended ERP and AI-integrated enterprise application.
Many learners who are new to Reinforcement Learning struggle early on because they do not clearly understand the conceptual difference and relationship between the Markov Decision Process (MDP) and Reinforcement Learning (RL). To reduce this confusion, it is necessary to first clarify how the two concepts are related.
