Learning Tetris with reinforcement learning
| dc.contributor.advisor | Filippakis, Michael | |
| dc.contributor.advisor | Φιλιππάκης, Μιχαήλ | |
| dc.contributor.author | Tsirovasilis, Ioannis | |
| dc.contributor.author | Τσιροβασίλης, Ιωάννης | |
| dc.date.accessioned | 2021-11-26T07:36:52Z | |
| dc.date.available | 2021-11-26T07:36:52Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | https://dione.lib.unipi.gr/xmlui/handle/unipi/13891 | |
| dc.identifier.uri | http://dx.doi.org/10.26267/unipi_dione/1314 | |
| dc.format.extent | 57 | el |
| dc.language.iso | en | el |
| dc.publisher | Πανεπιστήμιο Πειραιώς | el |
| dc.title | Learning Tetris with reinforcement learning | el |
| dc.type | Master Thesis | el |
| dc.contributor.department | Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών Συστημάτων | el |
| dc.description.abstractEN | The last decades, scientists have expressed an increasing interest for the game Tetris and more precisely for efficient algorithms that can score the most in-game points. A plethora of approaches have been tried out, including genetic algorithms, linear programming, cross-entropy and natural policy gradient, but none of them competes with experts players playing under no time pressure. In recent years, scientists have started applying reinforcement learning in Tetris as it displays effective results in adapting to video game environments, exploit mechanisms and deliver extreme performances. Current thesis aims to introduce Memory Based Learning, a reinforcement learning algorithm which uses a memory that helps in the training process by replaying past experiences. | el |
| dc.contributor.master | Ψηφιακά Συστήματα και Υπηρεσίες | el |
| dc.subject.keyword | Artificial intelligence | el |
| dc.subject.keyword | Deep learning | el |
| dc.subject.keyword | Reinforcement learning | el |
| dc.subject.keyword | Tetris | el |
| dc.subject.keyword | Neural networks | el |
| dc.date.defense | 2021-10-13 |
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Τμήμα Ψηφιακών Συστημάτων
Department of Digital Systems