Search results
A Python AI program that plays the traditional Japanese game Gomoku using Alpha-Beta Pruning Algorithm.
- GitHub - husus/gomokuAI-py: Implementation in python of a Gomoku AI ...
A simple Gomoku (or also called Five-In-A-Row) AI...
- GitHub - Mgla96/GomokuAI: AI that plays Gomoku using Mini-Max Algorithm ...
Gomoku is like tic-tac-toe except with larger game boards...
- GitHub - husus/gomokuAI-py: Implementation in python of a Gomoku AI ...
A simple Gomoku (or also called Five-In-A-Row) AI implemented in Python from scratch.
Gomoku is like tic-tac-toe except with larger game boards and the requirement of 5 of the same type of stone(a player's game piece) in a row to win. This AI uses the Minimax algorithm with Alpha-Beta Pruning and an optimized method of searching in order to determine the best move relatively quickly.
We have now obtained a neural network capable of imitating human players by predicting their next move, and plan to use this to jump-start the training of our AI Gomoku player using DeepMind’s reinforcement learning algorithm. Let us briefly describe how this would work.
By implementing the combination of heuristic and reinforcement algorithm using Python by ourselves, we are able to build a Gomoku AI player which can quickly learn the rules of Gomoku and eventually learns more advanced strategies, and to enable it with a much higher win percentage than its original version.
19 maj 2020 · In February 2020 I decided to code up Gomoku2049. The game is a demonstration of MiniMax, which is an algorithm for finding the move which minimizes the opponent’s best moves. This article is an...
12 maj 2018 · We combine a shallow neural network, which is trained by Adaptive Dynamic Programming (ADP), with Monte Carlo Tree Search (MCTS) algorithm for Gomoku. MCTS algorithm is based on Monte Carlo simulation method, which goes through lots of simulations and generates a game search tree.
