AlphaZero.jl: A Hackable Julia AlphaZero for One Desktop
A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.
At a glance
- What is it?
- AlphaZero.jl implements Deepmind's AlphaZero algorithm in roughly 2,000 lines of Julia, with generic game interfaces and cluster training that needs no code changes. The trade-off is that you inherit Julia's toolchain and a small ecosystem.
- Who is it for?
- Adopt AlphaZero.jl if you want to read and modify the whole AlphaZero loop, write your own game in Julia, and train on one GPU workstation or a small cluster without rewriting the trainer. Do not adopt it if your environment is Python, if you need an actively evolving framework, or if you want an implementation tuned for large distributed runs.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 7 days ago.
- What is it written in?
- Mainly Julia, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The gap AlphaZero.jl fills between Leela Zero and a textbook
Strong AlphaZero implementations tend to be written in low-level languages and tuned for large distributed clusters. The README names Leela Zero as the example and says plainly that this makes such code "hardly accessible for students, researchers and hackers." The stated motivation for AlphaZero.jl is to sit between those two extremes: simple enough to read, fast enough to run meaningful experiments on limited hardware. The README claims the core algorithm is about 2,000 lines of pure Julia, and that the implementation is one to two orders of magnitude faster than competing pure-Python alternatives, excluding recent JAX-based libraries. That comparison is the author's claim, not an independent measurement, and the README does not break it down per component. The intended user is someone who wants to modify the search or the training loop, not someone who wants a turnkey chess engine. If you only need a strong player for an existing game, this is the wrong layer to start at.
Generic interfaces, self-play, and where the tree search sits
The design centers on generic interfaces. The README says these "make it easy to add support for new games or new learning frameworks," and the documentation links a tutorial titled Solving Your Own Games for that path. The training loop is self-play: the README states that the Connect Four agent "is not exposed to the baselines during training and learns purely from self-play, without any form of supervision or prior knowledge." The neural network is trained to predict both a policy and a value, and at play time it is plugged into Monte Carlo tree search rather than acting on its own. The README reports an experiment where the network alone, playing the action with the highest prior probability at each state, is initially unable to win a single game but ends up significantly stronger than a minmax baseline that plans at depth 5 with a handcrafted heuristic. That is a useful sanity check on the value head, and it is also a reminder that the search and the network are separable pieces you can evaluate independently. The README does not give the shape of the network, the size of the replay buffer, or the MCTS parameters in the body text; those live in the hyperparameters reference page.
Getting a Connect Four agent training on one machine
The README gives a four-command sequence. Set an environment variable first to work around a plotting bug: export GKSwstype=100, described as avoiding "an occasional GR bug." Then clone the repository, enter it, instantiate the project environment with julia --project -e 'import Pkg; Pkg.instantiate()', and start training with julia --project -e 'using AlphaZero; Scripts.train("connect-four")'. The game name is a string argument to Scripts.train, which is the extension point for your own game once you have registered it. The README states that each training iteration takes about one hour on a desktop with an Intel Core i5 9600K and an 8GB Nvidia RTX 2070. That figure is hardware-specific and comes from a 2019-era machine, so treat it as a rough order of magnitude rather than a target. Hyperparameters are documented on a separate reference page rather than in the README, so budget time to read that page before you tune anything.
Distributed training without forking the trainer
The README makes a specific claim about scaling: 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 the strongest architectural statement in the material, and it is also the one with the least supporting detail in the README itself. There is no configuration snippet for the cluster case in the text supplied here, so the mechanism (how workers are launched, how self-play games are distributed, how gradients are aggregated) cannot be confirmed from this material alone. The JuliaCon 2021 talk is linked for further detail. If distributed training is your reason for choosing this package, read that talk and the package overview page before you plan hardware around it. The single-machine path, by contrast, is fully specified in the README.
Julia is the price of admission
This is a Julia package, and the practical cost is the Julia toolchain: a language runtime, a package manager, and a project environment that you instantiate per checkout. The README's own instructions use --project for every command, which means your game code, your dependencies, and the package version are pinned together in one environment. That is good for reproducibility and annoying if your existing pipeline is Python. The integration surface for a Python-based research group is not described in the material supplied here. There is also the question of the game interface itself: the README describes generic interfaces for new games but does not state in the body text what constraints a game must satisfy. The documentation's own-game tutorial is the place to check whether your environment is two-player, zero-sum, and turn-based in the shape the interface expects. If your problem is single-agent or cooperative, this is the wrong tool, and no amount of interface genericity changes that.
Release cadence and what it implies for upgrades
The release history is uneven. v0.5.5 arrived in December 2025, while v0.5.4 was in January 2023 and v0.5.3 in January 2022. There is a two-year gap before the latest release, and the last push to the default branch is dated 2026-09-09, so the repository is not dormant. For a package you pin with Pkg.instantiate(), that cadence is workable: you are not chasing a moving target, and a two-year-old tutorial is unlikely to have rotted. The flip side is that you should not expect rapid responses to interface changes you propose, and a long-lived fork is a realistic outcome if you need behavior the maintainer does not want. The licence is MIT, which is permissive and imposes no source-disclosure obligation on your own code; the repository also ships a CITATION.bib, and the README asks that you cite it in research papers that use the software. This is a description of the licence text, not legal advice.
AlphaGPU.jl and ReinforcementLearning.jl as the real alternatives
The README itself points at two Julia alternatives, and the contrast is concrete. 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 unbeatable performances when used with small neural networks and environments that support batch-simulation on GPU." That is a real fork in the road: if your environment can simulate batches on the GPU and your network is small, AlphaGPU.jl is the better fit; if you need to plug in an arbitrary game or swap the learning framework, AlphaZero.jl's generic interfaces are the reason to stay. ReinforcementLearning.jl is a broader framework offering composable environments, algorithms, and components through Julia's multiple dispatch, and the README states that future releases of AlphaZero.jl may build on it as its multithreaded and distributed RL support improves. That is a dependency to watch, not a current one. If your project is Python-first, none of these three is the natural choice.
Who should clone this and who should keep looking
Take it if you want to read every line of an AlphaZero loop, if you are comfortable in Julia, and if one GPU workstation is your budget. The README's Connect Four walkthrough is a complete path from clone to a trained agent, and the 2,000-line core means the search and training code is small enough to modify rather than merely configure. Skip it if your environment is Python, if your problem is not two-player zero-sum, or if you need the GPU-resident MCTS that AlphaGPU.jl targets. Before you invest, verify three things: that your game fits the interface documented in the own-game tutorial, that the hyperparameters reference covers the knobs you need, and that the cluster-training claim in the README is backed by the JuliaCon 2021 talk or the package overview page, since the README does not spell out the mechanism. If all three check out, the MIT licence and the pinned project environment make this a low-risk dependency to pin and modify.
Editorial conclusion
Adopt AlphaZero.jl if you want to read and modify the whole AlphaZero loop, write your own game in Julia, and train on one GPU workstation or a small cluster without rewriting the trainer. Do not adopt it if your environment is Python, if you need an actively evolving framework, or if you want an implementation tuned for large distributed runs. Before committing, verify that your game fits the two-player zero-sum interface the documentation describes, and check the docs for your Julia version against the v0.5.5 release notes.
Community notes