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researching on [https://en.wikipedia.org/wiki/Transfer_learning transfer learning] which bridges between [[Supervised Learning|supervised]] and [[Deep Learning|deep]] [[Reinforcement Learning|reinforcement learning]].
He holds a B.Sc. from [https://en.wikipedia.org/wiki/University_of_Trento University of Trento] in 2014, and a M.Sc. from [https://en.wikipedia.org/wiki/University_of_Groningen University of Groningen] in 2017 <ref>[https://www.linkedin.com/in/matthia-sabatelli-70370b93/ Matthia Sabatelli | LinkedIn]</ref>.
=Chess=At University of Groningen, Matthia Sabatelli worked on chess - the project work along with [[Zacharias Georgiou]], [[Evangelos Karountzos]] and [[Yaroslav Shkarupa]] dealt with [[Reinforcement Learning|reinforcement learning]] in simple [[Endgame|chess endgames]] such as [[KRK]] <ref>[[Zacharias Georgiou]], [[Evangelos Karountzos]], [[Yaroslav Shkarupa]], [[Matthia Sabatelli]] ('''2016'''). ''A Reinforcement Learning Approach for Solving KRK Chess Endgames''. [https://github.com/paintception/A-Reinforcement-Learning-Approach-for-Solving-Chess-Endgames/blob/master/project_papers/final_paper/reinforcement-learning-approach(2).pdf pdf]</ref>.In his M.Sc thesis , supervised by [[Marco Wiering]] and [[Valeriu Codreanu]], Matthia Sabatelli elaborates on [[Learning|learning]] to play chess with minimal [[Search|lookahead]], using [[Neural Networks#Deep|multilayer perceptrons]] versus [[Neural Networks#Convolutional|convolutional neural networks]] to approximate [[Stockfish|Stockfish’s]] [[Evaluation|evaluation]], also comparing two different [[Board Representation|board representations]] for the input layer
<ref>[[Matthia Sabatelli]] ('''2017'''). ''Learning to Play Chess with Minimal Lookahead and Deep Value Neural Networks''. Master's thesis, [https://en.wikipedia.org/wiki/University_of_Groningen University of Groningen], [https://www.ai.rug.nl/~mwiering/Thesis_Matthia_Sabatelli.pdf pdf]</ref>.
<ref>[https://dblp.org/pid/160/6434.html dblp: Matthia Sabatelli]</ref>
==2016 ...==
* [[Zacharias Georgiou]], [[Evangelos Karountzos]], [[Yaroslav Shkarupa]], [[Matthia Sabatelli]] ('''2016'''). ''A Reinforcement Learning Approach for Solving KRK Chess Endgames''. [https://github.com/paintception/A-Reinforcement-Learning-Approach-for-Solving-Chess-Endgames/blob/master/project_papers/final_paper/reinforcement-learning-approach(2).pdf pdf] <ref>[https://github.com/paintception/A-Reinforcement-Learning-Approach-for-Solving-Chess-Endgames GitHub - paintception/A-Reinforcement-Learning-Approach-for-Solving-Chess-Endgames: Machine Learning - Reinforcement Learning]</ref>
* [[Matthia Sabatelli]] ('''2017'''). ''Learning to Play Chess with Minimal Lookahead and Deep Value Neural Networks''. Master's thesis, [https://en.wikipedia.org/wiki/University_of_Groningen University of Groningen], [https://www.ai.rug.nl/~mwiering/Thesis_Matthia_Sabatelli.pdf pdf] <ref>[https://github.com/paintception/DeepChess GitHub - paintception/DeepChess]</ref>
* [[Matthia Sabatelli]], [[Francesco Bidoia]], [[Valeriu Codreanu]], [[Marco Wiering]] ('''2018'''). ''Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead''. ICPRAM 2018, [https://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/ICPRAM_CHESS_DNN_2018.pdf pdf]
