AutoGo: A Tutorial on Machine Learning for Go
Discover how the MCTS algorithm works in the game of Go.
1What is AutoGo?
AutoGo is an educational platform that uses machine learning techniques to learn and develop strategies for the game of Go. It focuses particularly on the Monte Carlo Tree Search (MCTS) algorithm.
2Basics of the Game of Go
Go starts with basic mechanics such as stone placement, capture, and ko rules. At the end of the game, scoring is done and strategic moves are determined.
3Monte Carlo Tree Search (MCTS)
MCTS is an algorithm that simulates possible moves to find the best strategy. This method yields effective results in complex games like Go.
4How MCTS Works
MCTS consists of four basic steps: selection, expansion, simulation, and backpropagation. These steps enable the algorithm to find the best move.
5The PUCT Formula
PUCT is a formula used in MCTS. This formula balances exploration and exploitation, making the algorithm work more efficiently.
6Training Process
AutoGo uses the MCTS algorithm to carry out a self-learning process. In this process, the algorithm continuously improves itself.
7Performance Improvement
AutoGo uses various techniques to improve the performance of the MCTS algorithm. These techniques enable the algorithm to deliver faster and more accurate results.
8Alternative RL Techniques
AutoGo also examines alternative reinforcement learning techniques alongside MCTS. These techniques are used to develop game strategies.
9Research Findings
AutoGo's research findings demonstrate how effective MCTS is in complex games like Go. These findings form an important basis for future studies.
Original article: Eric Jang — AutoGo: Makine Öğrenimi ile Go Oyunu Eğitimi