Teaching

I will give some lectures in this course, together with Matthia Sabatelli, where we will introduce some theoretical topics about reinforcement learning with focus on implementations aspects. In this course, we will see how one can train agents agents by characterizing RL algorithms both from a theoretical perspective as well as from a more practical one. To this end, six theoretical lectures will be given, the content of which will have to be, in part, put into practice in two different assignments and in a final project.

Literature : Reinforcement Learning: An Introduction, Richard S. Sutton, Andrew G. Barto

This course continues on the path of Imperative Programming and Algorithms and Data Structures, and gives an introduction to object-oriented programming. In particular, principles of object oriented programming in Python will be discussed.

Literature : Python Object-Oriented Programming - Fourth Edition, By Steven F. Lott, Dusty Phillips

Reinforcement Learning (RL) is the branch of machine learning that aims to teach agents how to interact with an environment through trial and error. Such interaction is usually modeled as a Markov Decision Process where the end goal of the agent, sometimes called the learner, is that of maximizing a certain reward signal. Unlike other machine learning approaches, such as the arguably more popular supervised learning one, RL is largely considered more challenging as an agent is deprived of any external supervision. Therefore, it can only rely on its own personal experience while learning. In this course, we will see how one can train such agents by characterizing RL algorithms from a theoretical perspective.

Literature : Reinforcement Learning: An Introduction, Richard S. Sutton, Andrew G. Barto

I will be giving a lecture in this course, where Matthia Sabatelli is the coordinator. The course aims to introduce the student to the field of Deep Reinforcement Learning; by the end of it the student will be familiar with Reinforcement Learning Basics, Deep Learning for Reinforcement Learning, Deep Q-Networks, Policy Gradients, Deep model-based RL, meta-learning, and transfer learning.

Literature :

• François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning.
• Plaat, A. (2022). Deep reinforcement learning