Reinforcement Learning for Operations Research

Traditional optimization methods often struggle with dynamic, real-world complexity. By combining reinforcement learning with operations research, you can train intelligent agents that adapt to changing conditions and solve complex decision-making problems. This text-based course guides you from the fundamental mathematical concepts of Markov Decision Processes to building practical Python solutions for scheduling, inventory management, and vehicle routing. You will learn to formulate operations research challenges as reinforcement learning environments and implement algorithms to solve them. What you'll learn: Understand the foundational concepts of Markov Decision Processes (MDPs) and dynamic programming; Formulate custom operations research problems into standard reinforcement learning environments using modern Gymnasium conventions; Implement Q-learning and policy gradient algorithms from scratch using clean, modern Python; Apply reinforcement learning agents to classic optimization problems like vehicle routing and resource allocation; Evaluate agent performance using modern validation patterns and reward-shaping techniques. You will start with core definitions and basic decision theory before moving on to hands-on Python code snippets. The course progresses from simple grid-world examples to complex, multi-variable operations research scenarios. Designed for beginners to reinforcement learning, this course requires only basic Python programming knowledge and a familiarity with introductory algebra. Start learning how to solve complex optimization challenges with intelligent agents today.