# AlphaZero.jl: a 2,000-line Julia implementation of AlphaZero you can actually read

> AlphaZero.jl reimplements DeepMind's AlphaZero in about 2,000 lines of Julia, with generic interfaces for games and learning frameworks. It is aimed at students, researchers and hackers who want to run experiments on one desktop machine, not at teams chasing superhuman Chess or Go.

**jonathan-laurent/AlphaZero.jl** — A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.

- Repository: https://github.com/jonathan-laurent/AlphaZero.jl
- Website: https://jonathan-laurent.github.io/AlphaZero.jl/stable/
- Stars: 1,334 · Forks: 147
- Language: Julia
- License: MIT
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/jonathan-laurent-alphazero-jl

## The gap AlphaZero.jl fills between Leela Zero and a notebook

AlphaZero is resource-hungry. The README says successful open source implementations such as Leela Zero are written in low-level languages like C++ and optimized for highly distributed computing environments, which makes them hard for students, researchers and hackers to approach. AlphaZero.jl takes the opposite position: the core algorithm is about 2,000 lines of pure Julia, and the stated motivation is an implementation simple enough to be widely accessible while still fast enough for meaningful experiments on limited computing resources. The README claims it is between one and two orders of magnitude faster than competing alternatives written in pure Python, excluding recent JAX-based libraries.

The audience is therefore narrow and specific. It is not people who want the strongest Chess engine, and it is not teams that already run distributed training infrastructure. It is people who want to read the search and the training loop, change a hyperparameter, and watch a small game get solved on a desktop with one GPU. The Connect Four example is the demonstration: the README reports that each training iteration takes about one hour on an Intel Core i5 9600K with an 8GB Nvidia RTX 2070, and the agent learns purely from self-play with no supervision or prior knowledge.

## Generic interfaces, a self-play loop, and one code path for laptop or cluster

The architecture visible from the repository is a small core with generic interfaces around it. The README states that generic interfaces make it easy to add support for new games or new learning frameworks, and the repository layout backs that up: src/ holds the package, games/ holds game definitions, scripts/ holds the training entry points, and test/ holds the test suite. The docs directory contains a tutorial on solving your own game, which is the path a new user follows to plug in a game that is not Connect Four.

The training mechanism is self-play plus search plus a neural network, the standard AlphaZero loop. The README describes the evaluation setup in a way that makes the loop concrete: the agent plays against a vanilla MCTS baseline and a minmax agent that plans at depth 5 with a handcrafted heuristic, and it is never exposed to those baselines during training. The same page also evaluates the neural network alone, playing the action with the highest prior probability at each state instead of plugging it into MCTS. That second evaluation is the interesting one for anyone debugging a training run: the README says the network alone is initially unable to win a single game but ends up significantly stronger than the minmax baseline without performing any search.

The distributed claim is the one to treat carefully. The README says the same agent can be trained on a cluster of machines as easily as on a single computer and without modifying a single line of code. That is a design promise, and the README gives a talk as the reference rather than a configuration example. Anyone who needs the cluster path should read the package overview and hyperparameter documentation before assuming the switch is free.

## Installing AlphaZero.jl and training a Connect Four agent

The README gives one command sequence, and it is short enough to reproduce directly. It sets an environment variable to avoid what the README calls an occasional GR bug, clones the repository, enters it, instantiates the Julia project, and starts training.

```sh
export GKSwstype=100  # To avoid an occasional GR bug
git clone https://github.com/jonathan-laurent/AlphaZero.jl.git
cd AlphaZero.jl
julia --project -e 'import Pkg; Pkg.instantiate()'
julia --project -e 'using AlphaZero; Scripts.train("connect-four")'
```

The first line exports GKSwstype=100 before anything else runs; the README presents it as a workaround for a GR plotting bug, so it belongs before the Julia commands. The clone and cd put you inside the repository, and Pkg.instantiate() resolves the project environment declared in Project.toml. The final command loads the package and calls Scripts.train with the string "connect-four". Note the exact spelling: the game name is passed as a string argument, and the entry point lives under Scripts, not in the package's top-level namespace.

What you should expect after that is a training run, not a finished agent. The README reports roughly one hour per training iteration on the hardware it names, and the training UI and explorer screenshots in the repository show what the run looks like while it is going. If you want a different game, the Connect Four tutorial and the solving-your-own-game tutorial are the two documents the README points to; the package overview and the hyperparameter reference are where the tunable parameters are described.

## Where AlphaZero.jl is the wrong tool

The most obvious limitation is scope. AlphaZero.jl is a general AlphaZero implementation, not a Chess or Go engine, and the README's own demonstration is Connect Four, a game far smaller than either. Nothing in the README claims competitive strength at Chess or Go, and the comparison it makes is against a vanilla MCTS baseline and a depth-5 minmax agent with a handcrafted heuristic. If your goal is a strong engine for a large game, the low-level, distributed implementations the README describes as hard to approach are the ones built for that job.

The second limitation is operational. The README gives one hardware profile for the timing claim, an Intel Core i5 9600K and an 8GB Nvidia RTX 2070, and one hour per iteration. It does not publish a hardware matrix, so you cannot extrapolate from that sentence to your own machine. It also does not document rollback or checkpoint recovery in the README text. The CHANGELOG.md exists at the repository root, and the releases page shows v0.5.5 on 2025-12-12 after a two-year gap from v0.5.4 in 2023, so anyone planning to depend on the package should read the changelog rather than assume a steady release cadence.

The third limitation is the interface cost. Generic interfaces are the selling point, but a new game still has to be expressed through them, and the README points to a tutorial rather than a specification. Budget time for that work before assuming the algorithm transfers cleanly to your domain.

## AlphaGPU.jl and ReinforcementLearning.jl as different bets

The README lists related Julia projects, and two of them are genuine alternatives with different trade-offs. AlphaGPU.jl is described as an AlphaZero implementation inspired by the paper "Scaling Scaling Laws with Board Games", where almost everything happens on the GPU, including the core MCTS logic. The README says it trades off some genericity and flexibility in exchange for performance with small neural networks and environments that support batch simulation on GPU. That is the opposite bet from AlphaZero.jl: if your environment can be batched on GPU and your network is small, AlphaGPU.jl is the one built for that shape of problem. If you need to plug in a game that cannot be batch-simulated, AlphaZero.jl's generic interfaces are the reason to stay.

ReinforcementLearning.jl is a different kind of alternative. The README describes it as a reinforcement learning framework that uses Julia's multiple dispatch to offer composable environments, algorithms and components, and notes that future releases of AlphaZero.jl may build on it as it gains better support for multithreaded and distributed RL. So it is not a drop-in replacement today; it is the ecosystem AlphaZero.jl might eventually sit inside. If your interest is composable RL components rather than AlphaZero specifically, start there. If your interest is reading and modifying AlphaZero's search and training loop, AlphaZero.jl is the smaller, more direct target.

## Licence, maintenance and the cost of upgrading

AlphaZero.jl is MIT licensed, which is permissive and places few obligations on reuse beyond preserving the licence notice. The repository also ships a CITATION.bib file, and the README asks that you cite the software if you use it in research, so the practical obligation for academic users is citation rather than licensing. This is not legal advice; read the LICENSE file at the repository root for the terms that actually apply.

On maintenance, the last push to the repository was on 2026-09-09, and the most recent release is v0.5.5 from 2025-12-12. The release history is uneven: v0.5.4 dates from 2023-01-04 and v0.5.3 from 2022-01-30, so there was a roughly two-year gap between v0.5.4 and v0.5.5. The repository is not archived, and the README states that contributions are welcome and points to a contribution guide. Anyone pinning this as a dependency should read CHANGELOG.md between versions rather than assume semantic-versioning guarantees, because the README does not describe a deprecation policy.

## Conclusion

Adopt AlphaZero.jl if you want to read, modify and run AlphaZero end to end on one machine, and if your game fits the generic interfaces the package exposes. Do not adopt it if you need superhuman Chess or Go, or a maintained training pipeline with published benchmarks beyond the Connect Four and grid-world examples. Before committing, check that your game can be expressed through the package's interfaces, and confirm the Julia and GPU setup your machine needs, because the README gives one command sequence and no hardware matrix.

## FAQ

### Is AlphaZero.jl the same as DeepMind's AlphaZero?

It is an implementation of DeepMind's AlphaZero algorithm, not the original system. The README describes it as a generic, simple and fast implementation whose core algorithm is about 2,000 lines of pure Julia, and its demonstrated example is Connect Four rather than Chess or Go.

### How do I install AlphaZero.jl and start training?

The README gives a four-step sequence: export GKSwstype=100, clone the repository and cd into it, run Pkg.instantiate() with julia --project, then call Scripts.train with the game name as a string, for example "connect-four".

### How long does training take with AlphaZero.jl?

The README reports about one hour per training iteration on an Intel Core i5 9600K with an 8GB Nvidia RTX 2070 GPU, using Connect Four as the example. It does not publish timings for other hardware or other games.

### Can I use AlphaZero.jl for my own game instead of Connect Four?

The README states that generic interfaces make it easy to add support for new games, and it links to a tutorial on solving your own game. The tutorial is the documented path; the README does not give a formal interface specification.

### Does AlphaZero.jl run on a cluster?

The README says the same agent can be trained on a cluster of machines as easily as on a single computer and without modifying a single line of code, and links to a talk as the reference. The README itself does not show a cluster configuration.

## Sources

- [jonathan-laurent/AlphaZero.jl on GitHub](https://github.com/jonathan-laurent/AlphaZero.jl)
- [License: MIT](https://github.com/jonathan-laurent/AlphaZero.jl/blob/master/LICENSE)
- [Project website](https://jonathan-laurent.github.io/AlphaZero.jl/stable/)
- [README](https://github.com/jonathan-laurent/AlphaZero.jl/blob/master/README.md)
- [Releases](https://github.com/jonathan-laurent/AlphaZero.jl/releases)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/jonathan-laurent-alphazero-jl