Aenteas/MCTS
A fast C++ implementation of fully customizable Monte Carlo tree search
This project offers a highly configurable tool for decision-making in complex board games by simulating possible moves and outcomes. It takes in the rules and current state of a two-player game and suggests the most advantageous next move. This is for game AI developers or researchers who want to implement advanced game-playing agents.
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Use this if you need a robust, high-performance C++ foundation for developing AI players for complex, two-player board games, where customization of search strategies is key.
Not ideal if you're looking for a general-purpose AI for other domains like reinforcement learning outside of board games, or if you prefer a simpler, less customizable solution.
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Language
C++
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Last pushed
Feb 16, 2023
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