Ph.D. Course: Reinforcement Learning for Autonomous Systems Design 2019-2020


The course will provide an introduction to Reinforcement Learning in the context of the design of intelligent systems, intelligent agents and intelligent machines. In particular, we will discuss multi-armed bandits, Montecarlo methods, tabular methods, approximation function methods, and Deep Reinforcement Learning.

We will consider a series of applications including classic control theory problems, robotics, games, recommender systems and distributed systems design.


Tuesday 1 September 9-11am 2-5pm

Monday 7 September 9-11am 2-5pm

Tuesday 8 September 9-11am

Tuesday 22 September 9-11am

Wednesday 23 September 9-11am


An extensive list of resources will provide during the module.

Teaching Material

It will be made available here before the lectures.

Delivery Modality

The module will be delivered through Microsoft Teams. The enrolled students will receive a link before the classes.


Students will be invited to present and discuss papers during the module.


Please contact me for enrolling in this module (mirco.musolesi[AT]


Introduction to the Course

Introduction to Reinforcement Learning

Multi-armed Bandits

Monte Carlo Methods

Temporal Difference Learning

Value Approximation Methods in Reinforcement Learning and Deep Reinforcement Learning

Last updated: 8 September 2020.