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Modelling of Pacman agents with neural networks being able to learn how to play Pac-Man video game using NeuroEvolution of Augmenting Topologies (NEAT) approach.

Java 98.31% Batchfile 0.55% Shell 1.13%

neuroevopacman's Introduction

Alessio Quercia

I am a Ph.D. Candidate in Computer Science at RWTH Aachen University and at the Machine Learning and Data Analytics Institute (IAS-8) at Forschungszentrum Jülich. I am supervised by Prof. Ira Assent, Dr. Hanno Scharr and Prof. Abigail Morrison.

My current research interests include Data Efficient Learning, Transfer Learning, Multi-Task Learning, Multi-Modal Learning. In general, I am interested in Computer Vision applications to real-world problems.

Short CV

Experience

[09.2019 - 07.2021] > Research Intern at IBM Research Zurich
[10.2018 - 07.2019] > Tutor of Computer Science Basics at University of Milan

Education

[05.2021 - Current] > Ph.D. in Computer Science at RWTH Aachen University and Forschungszentrum Jülich
[10.2020 - 05.2021] > Ph.D Candidate at Sapienza University of Rome and Research Assistant at WSense S.r.l.
[09.2017 - 04.2020] > MSc in Computer Science at University of Milan
[09.2013 - 07.2017] > BSc in Computer Science at Sapienza University of Rome

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neuroevopacman's Issues

FirstVersion

Clean up the code.
Comment the new
Write the input, the output and the fitness for the problem.
Add the possibility to start the evolution of a population of PacMan players.
Add the possibility to save/load an already trained neural network and test it.

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