Large-Scale Decision Making and Dynamic Programming

How do we make optimal decisions when systems grow too large and complex for standard algorithms? Managing resource allocation, financial portfolios, or queueing networks requires a structured approach to sequential decision-making under uncertainty. This text-only course guides you through the core concepts of large-scale dynamic programming, equipping you to model complex scenarios and deploy approximation methods to find practical solutions. What you'll learn: - Understand the foundational principles of Markov Decision Processes (MDPs) and sequential decision-making. - Apply classic dynamic programming algorithms to small and medium-scale problems. - Explore value function approximation techniques to handle high-dimensional state spaces. - Implement simulation-based algorithms and policy search methods to find optimal strategies. - Analyze real-world applications in dynamic resource allocation, finance, and queueing networks. - Integrate modern computational approaches, including basic reinforcement learning concepts, to scale up your solutions. You will start with fundamental definitions of state, action, and reward before progressing to advanced approximation architectures. Through step-by-step written explanations, mathematical breakdowns, and pseudocode examples, you will learn how to design and evaluate decision-making policies for complex systems. This course is designed for beginners, analysts, and engineers looking to understand decision theory and dynamic programming from the ground up, requiring only basic algebra and familiarity with fundamental programming concepts. Start mastering the mathematics and algorithms of complex decision-making today.