KEYWORD |
Comparison of Reinforcement Learning Frameworks
Thesis in external company
keywords IA, ARTIFICIAL INTELLIGENCE, DEEP LEARNING
Reference persons BARTOLOMEO MONTRUCCHIO
External reference persons Enrico Busto (www.add-for.com)
Research Groups GR-09 - GRAphics and INtelligent Systems - GRAINS
Thesis type RESEARCH
Description Reinforcement Learning (RL) is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment.
RL in conjunction with Deep Learning has obtained outstanding results in Atari video games, the Go board-game and a more complex environment like StarCraft II.
Recently many open source RL frameworks has been released by software companies in order to easily train and test new RL algorithms.
The goal of the thesis is to benchmark the most promising RL frameworks, to study the new algorithms proposed and to evaluate their performance on research environments.
Required skills Experience with Linux or Unix based OS;
Proficiency in at least one programming language (Python, Lua, Matlab, C++, Java);
Basic knowledge of machine learning;
Good knowledge of linear algebra.
Notes This thesis is ideal for students that are about to get their Master Degree in: computer science, computer engineering, mechatronic engineering, mathematical engineering, physics of complex systems, mathematics, physics or stochastic and data science.
Deadline 01/02/2020
PROPONI LA TUA CANDIDATURA