maxpumperla/deep_learning_and_the_game_of_go resource

Health v2 · maintenance only

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Adoption not part of the score

1056 stars · 408 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Companion code and pip-installable library (dlgo) for the Manning book 'Deep Learning and the Game of Go', implementing Go game AI from basic bots to AlphaGo and AlphaGo Zero techniques in one framework. It serves both as a learning resource for deep learning and reinforcement learning and as a working Go bot library.

Use cases

• learn deep learning by building a Go bot
• implement AlphaGo Zero from scratch
• build a Go engine with neural networks and tree search
• study reinforcement learning with policy gradients and actor-critic methods
• play against demo Go bots trained with deep learning
• teach a course on game AI

When to choose

• you want a structured, book-guided path into deep learning and game AI
• you want a working Python Go framework covering MCTS through AlphaGo Zero
• you are learning reinforcement learning with a concrete game example

When to avoid

• you need a production-strength Go engine to compete with modern bots like KataGo
• you need a general-purpose game AI framework beyond Go
• you need actively maintained software with a clear license

Facets

learning-resource · maturity maintenance

machine-learning deep-learning reinforcement-learning game-engine agent-framework deep-learning reinforcement-learning tutorials education python cross-platform go-game alphago alphago-zero book-companion monte-carlo-tree-search neural-networks game-ai baduk game-development

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