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.