Deep Reinforcement Learning for 5*5 Multiplayer Go
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913359728738304 |
|---|---|
| author | Driss, Brahim Arjonilla, Jérôme Wang, Hui Saffidine, Abdallah Cazenave, Tristan |
| author_facet | Driss, Brahim Arjonilla, Jérôme Wang, Hui Saffidine, Abdallah Cazenave, Tristan |
| contents | In recent years, much progress has been made in computer Go and most of the results have been obtained thanks to search algorithms (Monte Carlo Tree Search) and Deep Reinforcement Learning (DRL). In this paper, we propose to use and analyze the latest algorithms that use search and DRL (AlphaZero and Descent algorithms) to automatically learn to play an extended version of the game of Go with more than two players. We show that using search and DRL we were able to improve the level of play, even though there are more than two players. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14265 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for 5*5 Multiplayer Go Driss, Brahim Arjonilla, Jérôme Wang, Hui Saffidine, Abdallah Cazenave, Tristan Artificial Intelligence In recent years, much progress has been made in computer Go and most of the results have been obtained thanks to search algorithms (Monte Carlo Tree Search) and Deep Reinforcement Learning (DRL). In this paper, we propose to use and analyze the latest algorithms that use search and DRL (AlphaZero and Descent algorithms) to automatically learn to play an extended version of the game of Go with more than two players. We show that using search and DRL we were able to improve the level of play, even though there are more than two players. |
| title | Deep Reinforcement Learning for 5*5 Multiplayer Go |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2405.14265 |