Reinforcement Learning based Ultimate Tic Tac Toe player
-
Updated
Nov 15, 2018 - Python
Reinforcement Learning based Ultimate Tic Tac Toe player
AI-Bot for Ultimate-Tic-Tac-Toe in Python
Ultimate Tic-Tac-Toe game and agents. The following agents are implemented: random, Monte-Carlo, Min-Max, DQN
An AI agent implemented using Monte Carlo Tree Search (MCTS) using Upper Confidence Bounds (UCT).
Monte Carlo Tree Search algorithm on Ultimate TicTacToe
Temporal difference learning for ultimate tic-tac-toe.
Selfplay reinforcement learning for ultimate tic-tac-toe (UTTT)
AlphaZero for ultimate tic-tac-toe.
A bot that plays ultimate tic-tac-toe.
Ultimate Tic Tac Toe in Python over the console with experimental concept AI functionality
[WIP] Agents to play Ultimate Tic Tac Toe using algorithms such as Minimax, MCTS, Alpha-Beta Pruning
♟️ Ultimate tic-tac-toe game
Discord bot for Ultimate-Tic-Tac-Toe
Play Ultimate Tic-Tac-Toe simulated by Monte Carlo Tree Search
An implementation of the AlphaZero algorithm for Ultimate tic-tac-toe
AI-driven board game composed of nine tic-tac-toe boards arranged in a 3 × 3 grid.
A rule-based AI for playing Ultimate Tic Tac Toe!
A python implementation of an agent for ultimate tic-tac-toe using Monte Carlo Tree Search and Upper Confidential Bound
Add a description, image, and links to the ultimate-tic-tac-toe topic page so that developers can more easily learn about it.
To associate your repository with the ultimate-tic-tac-toe topic, visit your repo's landing page and select "manage topics."