wildbg

wildbg is a backgammon engine based on neural networks. Currently, it's in alpha stage.

As of January 2024, it reaches an error rate of roughly 5.9 for 1-pointers when being analyzed with GnuBG 2-ply.

For discussions, please join the Discord Server Computer Backgammon .

Goals

1. Provide source code and documentation to train neural nets from zero to super human strength.
2. Implement logic to evaluate all kind of backgammon positions: cubeless and cubeful equities, multi-ply evaluation, rollouts, etc.
3. Make the backgammon engine accessible via an easy-to-use HTTP JSON API.

A graphical user interface (GUI) is not part of this project.

Try it out

Bots on Backgammon Servers

bgammon.org

Thanks to @tslocum you can play against BOT_wildbg on his new backgammon server https://bgammon.org. No need to download a client or register.

The source code of the bot can be found here. There are also winning statistics available.

OpenGammon

On OpenGammon.org you can play against WildBG.

FIBS

Thanks to @oysteijo you can play against wildbg on the backgammon server FIBS. As FIBS client I recommend JavaFibs.

HTTP API

Online

You can access the API and see yourself how wildbg would move: https://wildbg.shuttleapp.rs/swagger-ui/

An example for the starting position and rolling 3 and 1: https://wildbg.shuttleapp.rs/move?die1=3&die2=1&p24=2&p19=-5&p17=-3&p13=5&p12=-5&p8=3&p6=5&p1=-2

Locally

Install Rust on your machine and then execute cargo run or cargo run --release. A web server will be started which you can access via http://localhost:8080/swagger-ui/

Beware that the networks committed to this repository are very small networks just for demonstration purposes. You can find the latest training progress and networks here: https://github.com/carsten-wenderdel/wildbg-training

Documentation

For users (bots and GUIs)

• HTTP API: https://wildbg.shuttleapp.rs/swagger-ui/
• C API: docs/user/wildbg-c.md

For contributors

• Code structure: docs/dev/architecture.md
• Engine: docs/dev/engine.md
• Training process: docs/dev/training.md

Also see the CHANGELOG for a list of changes.

Acknowledgments

This project is inspired and influenced by other backgammon engines:

• TD-Gammon by Gerald Tesauro brought the idea of using neural networks to backgammon
• GnuBG - The strongest open source backgammon engine

Thanks to JetBrains for providing a free license for their IDEs via their Open Source Support Program.

Contributing

Help is more than welcome! There are some smaller tasks but also bigger ones, see https://github.com/carsten-wenderdel/wildbg/issues. Currently, most needed is:

• Documentation of backgammon metrics / neural net inputs: #4
• Proper cube handling: #17

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.