Implementation of an AlphaGo Zero paper in one C++ header file without any dependencies.

To achieve that include/ai.h implements:

• Basic multilayer neural networks
• Multiple input and multiple output layers
• Convolution layers
• MSE and cross entropy loss
• Model serialization
• Extendable optimizer, currently: Adagrad, Adam, SGD
• Basic RL environment: game, player, replay buffer
• Monte Carlo Tree Tearch with policy NN
• Self-play, optimization, evaluation and validation workers

The idea is to have simple, minimal and easy to understand implementation. That means that performance was not a priority. Howeever the whole training process runs in parallel on 4 threads (4 workers) and optimizer also spawns some threads, it was easy to do that without sacrificing simplicity of the project.

Examples include (all single main.cpp files):

• Mnist CNN training
• Gradient checking
• Connect4 game RL training

Run premake.bat to generate solutions.

It was tested only on windows. But since it's just 2 files of code for every example, it should work out of the box. There is no platform specific code.