We will delve into an introduction to Reinforcement Learning (RL), which is applied in the context of decision-making to develop effective rules and strategies. Historically designed in an interactive context, we will also address the case of offline reinforcement learning. During this presentation, we will introduce the necessary mathematical formalism for RL, detailing the essential properties to navigate the vast array of available algorithms. Finally, we will discuss the main research directions in RL, with a digression into the application of RL in the medical field.
