AlphaZero: A Complete Guide to the Engine, Its Power, and Whether You Can Use It

AlphaZero is one of the most famous chess engines ever created, even though almost no everyday chess player has ever touched it.

Built by DeepMind, the same AI research lab behind AlphaGo, AlphaZero shocked the chess world with its creative style, its ability to teach itself from scratch, and its match victories against top engines. Many players today still refer to AlphaZero as a “revolutionary system” because it introduced a completely new way for computers to understand and play chess.

This guide explains what AlphaZero really is, how it works, how strong it is, and why it is not available for download or public use.

What Is AlphaZero?

AlphaZero is an AI system built by DeepMind that learns to play games—including chess—through reinforcement learning and self-play. Instead of using human knowledge, opening books, or handcrafted evaluation functions, AlphaZero starts with only the rules of the game. From there, it plays millions of games against itself, slowly improving through trial and error.

Unlike most chess engines, AlphaZero is not designed as a UCI engine or a program for ordinary computers. It is a research platform, built to demonstrate that generalized learning systems can master complex games without human guidance.

Key points:

• Developed by DeepMind, part of Alphabet (Google’s parent company).
• Learns chess from scratch without human input.
• Uses Monte Carlo Tree Search (MCTS) instead of alpha-beta pruning.
• Uses a deep neural network for evaluation and move prediction.
• Runs on specialized hardware (TPUs), not home PCs.
• Not publicly released, so users cannot download or use it.

How AlphaZero Works

Traditional chess engines rely on:

• brute-force search
• handcrafted evaluation
• massive pruning
• NNUE networks (in modern engines)

AlphaZero is completely different.

1. Neural Network Evaluation

AlphaZero uses a deep neural network that produces two outputs for each board position:

• a value (probability of winning, losing, or drawing), and
• a policy (probability distribution of strong moves).

This combination is similar to how humans think:
“What moves look promising?” and “Is the position good or bad?”

2. Monte Carlo Tree Search (MCTS)

Unlike classical engines that explore millions of moves per second, AlphaZero explores fewer variations but more intelligently. MCTS simulates many potential outcomes for promising moves, gradually refining its understanding through repeated search cycles.

3. Reinforcement Learning (Self-Play)

AlphaZero learns entirely by playing itself—starting with random moves. Through millions of games, it adjusts its network weights based on trial and error. No databases or opening books are used.

This approach produced a system with a very human-like, strategic, and fluid style of play.

How Strong Is AlphaZero?

Although DeepMind never published a formal Elo rating, the few matches it played were astonishing. In its famous 2017 experiment, AlphaZero:

• beat Stockfish 8 convincingly (28 wins, 0 losses, 72 draws),
• produced brilliant positional sacrifices,
• and demonstrated a deep, intuitive understanding of long-term initiative.

Of course, the match conditions were debated—Stockfish couldn’t use a large opening book or tablebases, and time controls were unusual. But the message was still clear:
AlphaZero was extremely strong and fundamentally different.

Since then, DeepMind has not released new match results. It’s widely believed that:

• AlphaZero today would be stronger than its published version,
• but modern engines like Stockfish NNUE, Berserk, Dragon, and Lc0 have surpassed the original AlphaZero in raw strength.

Still, AlphaZero’s influence is undeniable. Its ideas changed the entire engine landscape.

Can You Use AlphaZero?

Short answer:

No. AlphaZero is not available to download, install, or use.

Long answer:

DeepMind never released AlphaZero’s code, neural network, or training files. The system runs on TPUs (Tensor Processing Units)—specialized Google hardware—not on home CPUs or GPUs. AlphaZero was a research demonstration, not a consumer tool or competitive engine.

This means:

• You cannot download AlphaZero.
• You cannot run AlphaZero in a GUI.
• You cannot play against AlphaZero.
• You cannot reproduce its network.

Any website or file claiming to offer “AlphaZero download” is fake or refers to something else.

How to Download AlphaZero.jl from SourceForge

If you want to experiment with an open-source implementation of the AlphaZero idea, you can download AlphaZero.jl from SourceForge. Here’s how:

1. Go to the SourceForge project page
   Visit the AlphaZero.jl mirror on SourceForge. You’ll see the project description and available files.
2. Open the “Files” section
   Inside the Files tab, you’ll find versions such as v0.5.4.zip or v0.5.4.tar.gz. These contain the full source code.
3. Choose the file you want to download
   – If you prefer a ZIP file, select v0.5.4.zip and click Download.
   – If you prefer a tar.gz file, choose v0.5.4.tar.gz and download it.
   Both contain the same code; only the compression format is different.
4. Check the README
   You can also download the README file on SourceForge. It explains installation steps, how the project works, and how to run training scripts.
5. Install using Julia
   AlphaZero.jl is written in Julia, so you’ll need to install Julia on your computer to use it.
   The original GitHub repo shows how to clone the project and install dependencies using commands like: git clone <repo> julia --project Then you can run the training and test scripts included in the project.

Important Note

AlphaZero.jl is not the original DeepMind AlphaZero engine.
It is an open-source educational and research implementation that tries to follow the AlphaZero algorithm. The real, extremely strong AlphaZero built by DeepMind remains closed-source, and the official engine is not available for download anywhere.

So What Can You Use Instead?

Because AlphaZero was never released, several open-source projects were created to imitate its ideas using consumer hardware.

1. Leela Chess Zero (Lc0)

Lc0 is the most faithful AlphaZero-style engine:

• uses neural networks
• uses MCTS
• grows through community-driven self-play
• can run on consumer GPUs

This is the closest experience to AlphaZero available today.

2. Ceres

Ceres is a more experimental AlphaZero-like engine that uses modern deep learning concepts. It is pure MCTS and neural evaluation.

3. Maia

Maia is a neural engine focused on predicting human moves rather than maximizing strength. While different from AlphaZero, it also uses deep-learning ideas.

Why AlphaZero Cannot Be Downloaded

There are several reasons:

1. Proprietary Technology

AlphaZero is part of DeepMind’s research portfolio. The algorithms and training infrastructure are proprietary.

2. Requires Specialized Hardware

AlphaZero was trained on hundreds of Google TPUs. It cannot run on a normal laptop or gaming PC.

3. Not Intended for Public Use

DeepMind never planned AlphaZero as a user-facing chess engine. It was built to prove a concept, not to be deployed widely.

4. Security and Infrastructure

Releasing such a powerful, scalable learning system could raise concerns about uncontrolled use.

What We Can Learn from AlphaZero

Even though we cannot download AlphaZero, its influence reshaped the chess world. Here are some lessons it taught the community:

1. Creativity Is Not Just Human

AlphaZero’s attacking style—long-term sacrifices, king attacks, and dynamic pressure—showed that computers can be artistic.

2. Neural Networks Are the Future

After AlphaZero, many engines began adopting neural evaluation:

• Stockfish → NNUE
• Lc0 → GPU networks
• Berserk → hybrid models

3. Search Isn’t Everything

AlphaZero explores fewer positions than classical engines but evaluates them more intelligently.

4. Self-Play Is Extremely Powerful

AlphaZero learned world-class chess without human knowledge. This idea helped inspire new AI research far beyond chess.

FAQ: AlphaZero and AlphaZero.jl

1. Can I download the real AlphaZero engine created by DeepMind?

No. The original AlphaZero built by DeepMind is not available for download. It is a proprietary research system, and DeepMind has never released the engine, networks, or training code publicly.

2. So what exactly is AlphaZero.jl?

AlphaZero.jl is an open-source educational and research project written in Julia. It follows the general AlphaZero algorithm but is not the official engine. It is meant for experimentation, learning, and small-scale research.

3. Is AlphaZero.jl as strong as the real AlphaZero?

Not at all.
The original AlphaZero trained on enormous hardware resources (TPUs) and millions of self-play games. AlphaZero.jl is much lighter, built for personal computers and research purposes, so its strength is far below DeepMind’s version.

4. Is AlphaZero.jl strong enough to beat Stockfish or top engines?

No.
It’s not designed to compete with modern chess engines. Instead, its purpose is to help developers, students, and researchers understand how reinforcement learning systems like AlphaZero work.

5. Can AlphaZero.jl play chess out of the box?

It can play chess after training, but it usually starts with no knowledge. You must train it using self-play so the neural network learns to evaluate positions and choose moves.

6. What games does AlphaZero.jl support?

The project is designed to be generic, meaning it can support many board games.
Common experiments include:

• Chess
• Connect Four
• Tic-Tac-Toe
• Othello/Reversi
  However, chess is the most popular use.

7. Do I need a powerful computer to run AlphaZero.jl?

Not necessarily.
You can run small trainings on a normal desktop or laptop. But stronger training (more simulations, bigger networks) requires more CPU/GPU power.

8. How do I install AlphaZero.jl?

You download it from SourceForge or GitHub, unzip it, and run it using Julia.
Most users follow these steps:

1. Install Julia.
2. Download the AlphaZero.jl files.
3. Open a Julia environment.
4. Install dependencies using the project’s instructions.
5. Run training scripts or experiments.

9. Can I load AlphaZero.jl into a chess GUI like Arena or Cute Chess?

Not directly.
AlphaZero.jl is not a UCI engine. It’s a research framework, not a plug-and-play chess engine. To use it in a GUI, you would have to write your own wrapper that converts its move-selection logic into UCI commands.

10. Is AlphaZero.jl beginner-friendly?

It depends.
If you’re comfortable with programming—especially Julia—it’s fairly approachable. But for pure chess players with no coding background, the learning curve may feel steep.

11. Is AlphaZero.jl still maintained?

The original GitHub project saw active development mainly in 2020–2022.
It is stable and usable, but not frequently updated today. The SourceForge mirror simply hosts copies of existing releases.

12. Does AlphaZero.jl come with pre-trained chess networks?

Usually no.
Most users must train their own models. Training even a “medium-strength” network takes time and computing power.