Lecture
This lecture provides an introduction to reinforcement learning (RL), outlining its fundamental concepts and applications. It begins with an overview of the course structure and objectives, emphasizing the importance of RL in various fields such as automation, finance, and robotics. The instructor discusses classical definitions of RL, highlighting the process of learning to map situations to actions to maximize rewards. Key themes include the interaction between agents and environments, the significance of rewards, and the challenges posed by non-stationary data and delayed feedback. The lecture also covers the perceptions of RL across different disciplines, including control theory and machine learning. The instructor presents examples of RL applications, such as self-driving cars and game-playing AI, and discusses the theoretical foundations necessary for understanding RL algorithms. The session concludes with a preview of upcoming topics, including dynamic programming and policy iteration, setting the stage for deeper exploration of RL methodologies in subsequent lectures.