Digital Transformation Part Two - Foundations of Intelligent Agents AI (Agent AI): Decision-Making Principles and Applications of Python Reinforcement Learning

As generative AI enters the commercial stage, the next battlefield for businesses' digital transformation has shifted towards 'intelligent systems with autonomous decision-making capabilities (Agent AI)'. Reinforcement learning technology enables AI to continuously learn optimal strategies in dynamic and uncertain environments. Its application scenarios are rapidly expanding, from logistics delivery, inventory management, and production scheduling to financial trading decisions. This course is an advanced extension of 'Digital Transformation Part One - From Predictive AI to Generative and Decision-Making AI: Implementation of Graph Neural Networks and Deep Generative Modeling in Python', providing an in-depth analysis of reinforcement learning frameworks and Agent AI design methods. It helps learners break through the limitations of traditional rule-based systems, creating a decision-making hub with environmental awareness, real-time feedback, and strategy iteration capabilities, establishing learners' advanced skills in digitalization and intelligent transformation, and applying AI technologies in their work domains. The course covers Python implementation cases, such as inventory replenishment, dynamic pricing, financial trading, and reinforcement learning based on user feedback, enabling learners to utilize data analysis technologies while capturing trends in AI development. The course offers a [Digital Synchronous Learning] registration option, and we welcome students to enroll.