Foundations of Reinforcement Learning and Decision Making

Understanding how automated systems make optimal choices in complex, changing environments is a critical skill in modern artificial intelligence. Reinforcement learning provides the mathematical framework for training agents to maximize rewards through trial and error. In this foundational course, you will explore the mechanics of sequential decision making. You will start with the basic principles of utility theory and progress to building algorithms that allow intelligent agents to learn from their environments, balancing exploration with exploitation. What you'll learn: • Understand core terminology, utility theory, and how to model machine preferences. • Solve simple decision challenges using multi-armed bandit strategies. • Model complex environments using finite Markov Decision Processes (MDPs). • Apply dynamic programming techniques to evaluate and improve decision policies. • Practice structuring foundational algorithms using modern Python simulation environments. • Explore the basic concepts that bridge classic tabular methods to modern deep reinforcement learning. The curriculum flows from foundational definitions and mathematical models to practical algorithm design. You will read through step-by-step written explanations and study clear code snippets that translate abstract theories into working logic. This course is designed for beginners to artificial intelligence; no prior reinforcement learning experience is required. Start your journey into intelligent decision making today and build the foundation for modern AI development.