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.