XUNIK8/Reinforcement-Learning-in-Tsumego
利用强化学习、基于蒙特卡洛树搜索的UCT算法解决围棋死活题问题。Inplement improved Reinforcement Learning and UCT algorithms (based on Monte Carlo Tree Search) on Go/Tsumego problems
This project helps Go players and enthusiasts analyze and solve Tsumego (Go life-and-death problems). It takes a Tsumego problem as input and provides a visual simulation where a reinforcement learning AI, based on advanced Monte Carlo Tree Search, plays against itself or a human to determine the optimal solution. The output is a clear win/loss outcome (black wins or white wins) for the given problem, helping users understand complex Go positions.
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Use this if you want an intelligent system to help you analyze and find solutions for challenging Go life-and-death problems.
Not ideal if you're looking for a general Go playing AI for full-length games rather than specific Tsumego problem-solving.
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15
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3
Language
Python
License
Apache-2.0
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Last pushed
Dec 26, 2022
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