Reinforcement Learning
Objectives
The goal of this course is to provide students an overview of reinforcement learning, a very debated machine learning subfield. Reinforcement learning is the process of creating algorithms that learn to predict and behave in a stochastic environment based on previous experience. Reinforcement learning has a wide range of applications, from traditional control issues like engine optimization or dynamical system control to game play, inventory control, and a variety of other domains.
General characterization
Code
200295
Credits
4.0
Responsible teacher
Nuno Tiago Falcão Alpalhão
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
TBD
Bibliography
Teaching method
TBD
Evaluation method
TBD
Subject matter
- Introduction to Reinforcement Learning
- Sequential Decision-Making
- Markov Decision Processes
- Value Functions & Bellman Equations
- Dynamic Programming
Programs
Programs where the course is taught: