AlphaGo

5 Steps

1. 1

   Understand the Basics of Go: Familiarize yourself with the rules and basic strategies of the game Go. While you don't need to become an expert, understanding the game's complexity is crucial.

2. 2

   Explore Monte Carlo Tree Search (MCTS): Learn about Monte Carlo Tree Search, a search algorithm used in AlphaGo to explore the game tree and evaluate potential moves. Understand the four main steps: selection, expansion, simulation, and backpropagation.

3. 3

   Study Deep Neural Networks: Gain knowledge of deep neural networks, particularly convolutional neural networks (CNNs), which were used in AlphaGo to predict moves and evaluate board positions. Focus on understanding how CNNs can extract features from images (in this case, the Go board).

4. 4

   Learn about Reinforcement Learning: Understand the principles of reinforcement learning (RL), the technique used to train AlphaGo. Focus on how RL algorithms learn from rewards and penalties, and how AlphaGo used self-play to improve its strategy.

5. 5

   Implement a Simplified Go AI: Create a simplified Go AI using MCTS and a basic evaluation function. This will help you understand the core concepts of AlphaGo without the complexity of deep neural networks. You can use a library like PyGo or similar to handle the game logic.