Bertinoro International Summer School 2026
An Introduction to Reinforcement Learning: from Fundamentals to AI Agentic Systems
Overview
This course provides an introduction to reinforcement learning (RL), guiding participants from core concepts to the emerging field of AI agentic systems. We will introduce the fundamental principles that enable intelligent agents to learn through interaction with their environment, and we will explore the role of Markov Decision Processes (MDPs) in modelling sequential decision-making problems. Building on these foundations, we will discuss recent advances in deep reinforcement learning and the development of autonomous, goal-directed AI agents. Through conceptual explanations and illustrative examples, participants will gain insight into how RL algorithms enable systems to adapt, optimise decisions, and operate in complex environments. By the end of the course, participants will have a clear understanding of the basic theory behind reinforcement learning and its growing importance in the design of intelligent and agentic AI systems, providing a foundation for further study or practical experimentation in this rapidly evolving field.
Teaching Material
Introduction to Reinforcement Learning
Value Function Approximation in Reinforcement Learning
Last updated: 28 May 2026.