000983L - REINFORCEMENT LEARNING IN ARTIFICIAL INTELLIGENCE

Introduction to the basic RL principles, with a particular emphasis to their applications to combinatorial games.



LEARNING OUTCOMES



KNOWLEDGE AND UNDERSTANDING



At the end of the course the student should:

-) understand agent-environment interaction in MDP;

-) recognize the main differences among different RL principles;

-) know the most important RL algorithms.



APPLYING KNOWLEDGE AND UNDERSTANDING



At the end of the course the student should be able to:

-) understand whether a certain problem is well-suited for RL;

-) model a decision task as MDP;

-) work in the model-free case with both MC and TD methods;

-) implement from scratch a RL pipeline able to learn a simple combinatorial game.



COMMUNICATION SKILLS:



At the end of the course the student should be able to communicate RL concepts with a proper and sound language.



LEARNING SKILLS:



At the end of the course the student should be able to read and partially understand textbooks and research papers on RL.