AlphaZero Gomoku: Paper and Code - CatalyzeX
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AlphaZero Gomoku. Click To Get Model/Code. In the past few years, AlphaZero's exceptional capability in mastering intricate board games has garnered considerable interest. Initially designed for the game of Go, this revolutionary algorithm merges deep learning techniques with the Monte Carlo tree search (MCTS) to surpass earlier top-tier methods. In our study, we broaden the use of AlphaZero to Gomoku, an age-old tactical board game also referred to as "Five in a Row." Intriguingly, Gomoku has innate challenges due to a bias towards the initial player, who has a theoretical advantage. To add value, we strive for a balanced game-play. Our tests demonstrate AlphaZero's versatility in adapting to games other than Go. MCTS has become a predominant algorithm for decision processes in intricate scenarios, especially board games. MCTS creates a search tree by examining potential future actions and uses random sampling to predict possible results. By leveraging the best of both worlds, the AlphaZero technique fuses deep learning from Reinforcement Learning with the balancing act of MCTS, establishing a fresh standard in game-playing AI. Its triumph is notably evident in board games such as Go, chess, and shogi.
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
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Lessons from AlphaZero (part 3): Parameter Tweaking, by Aditya Prasad, Oracle Developers
AlphaZero paper peer-reviewed is available · Issue #2069 · leela-zero/leela-zero · GitHub
Chao Yu - CatalyzeX
Lessons from AlphaZero (part 3): Parameter Tweaking, by Aditya Prasad, Oracle Developers
Multiplayer AlphaZero – arXiv Vanity
AlphaZero paper peer-reviewed is available · Issue #2069 · leela-zero/leela-zero · GitHub
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