Reversi player using Monte Carlo Tree Search
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Updated
Aug 26, 2018 - Python
Reversi player using Monte Carlo Tree Search
An implementation of MinMax and Monte Carlo Tree Search solvers for Connect 4.
Fork of the "🤗 Transformers" repository. Extended to support the following decoding methods: MCTS, Stochastic Beam Search, Value-Guided Beam Search. Codebase extended on the understanding-decoding branch.
The project includes an implementation of the Alpha zero algorithm based on tictactoe and connect4 games using the keras library along with a game module to play with the algorithm. It is also possible to add more games in which two players make alternate moves.
A chess program based on Deep Mind's AlphaZero.
AI Playground for the game of Sogo, inspired by the Alpha Go Zero algorithm.
Using reinforcement learning to play games.
This is a python implementation of the board game Othello with Negamax and MCTS. Powered by Numba for high-performance computation.
Udacity - Artificial Intelligence - Project 3 (Adversarial Search) - Minimax, Alpha-Beta-Pruning, MCTS, Opening Book - All Files - Passed Mon 20 Aug 2018
Python Implementations of Monte Carlo Tree Search
A python implementation of an agent for ultimate tic-tac-toe using Monte Carlo Tree Search and Upper Confidential Bound
A collection of combinatorial games.
Deep reinforcement learning with Monte Carlo Tree Search
Naive Implementation of AlphaZero (WIP)
An exploratory project to learn and implement Reinforcement Learning approaches to solve simple games. Initial approaches to RL will include Monte Carlo Tree Search (MCTS) and Deep Q-Networks (DQN)
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