Reinforcement Learning

Learning Goals

Learning fundamental concepts and algorithms in decision-making theory and Reinforcement Learning and how to use Reinforcement Algorithms to identify optimal and approximately optimal strategies for decision-making in simple and complex environments.

Program in pills

Markov Decision Processes, Model-based Dynamic Programming, Partially Observable Markov Decision Processes, Model-free Reinforcement Learning, Temporal Difference Methods, Reinforcement Learning with function approximation, Policy optimization methods, Multi-agent Reinforcement Learning

Area

Machine Learning and Artificial Intelligence

Curriculum Foundations
TAF Type

B

Curriculum Industry
TAF Type

B

Curriculum Health
TAF Type

Curriculum Economy
TAF Type

SSD

INF/01

ECTS

6

Semester

2

Lecturers

Antonio Celani
Panizon Emanuele