Research Paper about adversarial search
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Updated
Apr 28, 2022 - Python
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Research Paper about adversarial search
Materials for AlphaGo
An asynchronous implementation of AlphaZero, a self-play reinforcement learning algorithm.
Lightweight AlphaZero implementation for Gomoku with MCTS + residual policy/value network, including ready-to-run 9Ă—9 and 15Ă—15 training presets.
I researched and explained AlphaGo/AlphaGo Zero papers, which had beaten the world the game of Go champion in 2016, 2017. Especially, I applied Alpha Zero algorithm to Othello to catch the whole idea.
A clean implementation of a self-play learning algorithm based on AlphaGo Zero (any game, any framework!) + tutorial
A browser-based Gobang game with multiple AI opponents, including a neural network powered AlphaZero-style agent.
I am interested in gobang,so I am going to use python to draw GUI and program a powerful AI to have fun! I will implement my idea by alpha-beta algorithm and alphago-zero algorithm.
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A replica of the AlphaZero methodology for deep reinforcement learning in Python
A free, open, curated library for studying Go / Baduk / Weiqi — game records, life & death, joseki, whole games, books & reference, organized by topic and level.
HybridAlpha - a mix between AlphaGo Zero and AlphaZero for multiple games
🎮 Enhance decision-making in game environments with the Predictor + UCT (PUCT) library, simplifying research in Monte Carlo Tree Search.
Computer go engine using Monte-Carlo Tree Search written in Python3.
Deep Reinforcement Learning to Play 2048 (with Keras)
Personal implementation of deep learning algorithms in the RL space to learn.
Java based alpha zero reinforcement learning. The core module suppports implementing any adversary board game. Example implementation for Tic Tac Toe.
Server side code of the SAI project
An implementation of the AlphaZero algorithm for Ultimate tic-tac-toe
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