Games, synthesis and control

Part 1:

Introduction to game theory for verification and synthesis. The synthesis of open systems or controllers is based on the principle of a reactive system, which must interact with its environment. The two entities - system and environment - are seen as 2 antagonistic players. Different types of games will be discussed: two-player games on finite arenas, games for controller synthesis, and distributed games.

Part 2:

Theoretical foundations of reinforcement learning, an artificial intelligence framework that enables agents to acquire knowledge and make decisions by interacting with their environment. We will explore in detail fundamental concepts such as the one-armed bandit problem, Markov decision processes, trade-offs between exploration and exploitation, Q-learning, etc., as well as advanced techniques such as function approximation and Deep Q-learning. By the end of this course, you'll have acquired the skills needed to understand AlphaGo, the first AI to outperform humans at the game of go.