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.
Reinforcement Learning
Learning Goals
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
Curriculum Industry
TAF Type
Curriculum Health
TAF Type
Curriculum Economy
TAF Type
SSD
ECTS
Semester
Lecturers
Antonio Celani
Panizon Emanuele