The "INTRODUCTION TO REINFORCEMENT LEARNING" course provides a comprehensive overview of reinforcement learning, a prominent branch of machine learning focused on training agents to make sequential decisions in dynamic environments. Through this, participants will explore the foundational concepts, algorithms, and methodologies underlying reinforcement learning. The course covers key topics such as Q-learning, deep Q-networks (DQN), policy gradient methods, actor-critic methods, and model-based reinforcement learning. Participants will also delve into challenges associated with reinforcement learning, including addressing limitations with deep neural networks and dealing with continuous action spaces. Additionally, the course discusses future trends shaping the evolution of reinforcement learning technologies.

Who This Course Is For: This course is designed for students, professionals, researchers, and enthusiasts interested in gaining a fundamental understanding of reinforcement learning. It is suitable for individuals with backgrounds in computer science, machine learning, artificial intelligence, or related disciplines, who wish to explore the principles and applications of reinforcement learning algorithms. Whether participants are beginners seeking to grasp the basics of reinforcement learning or experienced practitioners aiming to deepen their knowledge, this course offers valuable insights into the core concepts and challenges within the field.