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

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.

The training process consists of three steps, which are repeated in a loop:

1. Find lots positions for later rollout. Currently, rather random self play is used; later we want to make sure to find all kind of positions, including backgames.
2. Roll out these positions. Currently, only 1-ply rollouts are possible.
3. Train neural networks based on the rollout data. Currently, a single net with one hidden layer is supported; later different (and deeper) nets for different game phases are planned.

Already implemented is:

• Roll out a certain position 1296 times, multithreaded.
• 202 neural net inputs similar to TD-Gammon, representing the raw board.
• Find 100,000 random positions through self play for later rollouts.
• Train a single neural net with one hidden layer via PyTorch and save the result as an ONNX file. The net has six outputs for winning/losing 1, 2 or 3 points.
• Inference of that neural net in Rust via tract.

No neural network is committed to this repository. You can find the training progress and early rather weak networks here: https://github.com/carsten-wenderdel/wildbg-training

Currently only cubeless equities and moves are implemented. Cubes and cubeful equities are missing.

Ideation phase.

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: carsten-wenderdel#4
• Implementation of a bearoff database: carsten-wenderdel#1

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.

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.