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GR-09 - GRAphics and INtelligent Systems - GRAINS

Comparison of Reinforcement Learning Frameworks

azienda 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.

See also  [1] Reinforcement Learning: An Introduction - http://incompleteideas.net/book/the-book.html [2] Deep-Q-Network - https://www.nature.com/articles/nature14236

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




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