Summary of Deep Reinforcement Learning For 5*5 Multiplayer Go, by Brahim Driss et al.
Deep Reinforcement Learning for 5*5 Multiplayer Go
by Brahim Driss, Jérôme Arjonilla, Hui Wang, Abdallah Saffidine, Tristan Cazenave
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to extending computer Go from its traditional two-player format to games involving multiple players. Building on recent advances in search algorithms (Monte Carlo Tree Search) and Deep Reinforcement Learning (DRL), the authors utilize AlphaZero and Descent algorithms to enable computers to learn and improve their gameplay in extended Go scenarios. The results demonstrate that these techniques can lead to a significant improvement in playing level, even with more than two players involved. The paper contributes to the development of intelligent game-playing systems and has implications for applications such as multi-agent decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a game where multiple people play together instead of just two. Computers are really good at playing games like Go, but they usually play with only one opponent. This paper explores new ways to teach computers to play Go with more than two players. The authors use special algorithms that help computers learn and improve their gameplay. They found that these algorithms work well even when there are multiple players involved. This research can help us build better computer systems for games and other applications where multiple people make decisions together. |
Keywords
» Artificial intelligence » Reinforcement learning
