With this, AlphaZero was born — the general algorithm for getting good at something, quickly, without any prior knowledge of human expert strategy. - Applied Data Science

see also

• From-scratch implementation of AlphaZero for Connect4

# Ref

• AlphaGo Zero - How and Why it Works
• Mastering the game of Go without human knowledge
• AlphaGO zero cheat sheet

# Learning Algorithm summary

• Mentally play through possible future scenarios, giving priority to promising paths, whilst also considering how others are most likely to react to your actions and continuing to explore the unknown.

• After reaching a state that is unfamiliar, evaluate how favourable you believe the position to be and cascade the score back through previous positions in the mental pathway that led to this point.

• After you’ve finished thinking about future possibilities, take the action that you’ve explored the most.

• At the end of the game, go back and evaluate where you misjudged the value of the future positions and update your understanding accordingly.

# Code

see also AlphaGo Zero cheat sheet

• Jupyter notebook for DeepReinforcementLearning (github)

# Dependencies to add to docker images

pip3 install matplotlib
pip3 install pydot pydot-ng
pip3 install graphviz

apt-get update && apt-get install graphviz