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https://github.com/wuthefwasthat/hanabi.rs
State of the art Hanabi bots + simulation framework in rust
https://github.com/wuthefwasthat/hanabi.rs
Last synced: 4 days ago
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State of the art Hanabi bots + simulation framework in rust
- Host: GitHub
- URL: https://github.com/wuthefwasthat/hanabi.rs
- Owner: WuTheFWasThat
- Created: 2016-03-06T09:05:46.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-11-17T05:12:29.000Z (12 months ago)
- Last Synced: 2024-11-05T17:49:41.040Z (8 days ago)
- Language: Rust
- Homepage:
- Size: 238 KB
- Stars: 43
- Watchers: 4
- Forks: 11
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Simulations of Hanabi strategies
Hanabi is a cooperative card game of incomplete information.
Despite relatively [simple rules](https://boardgamegeek.com/article/10670613#10670613),
the space of Hanabi strategies is quite interesting.
This project provides a framework for implementing Hanabi strategies in Rust, and also implements extremely strong strategies.
The best strategy is based on the "information strategy" from the paper ["How to Make the Perfect Fireworks Display" by Cox et al](https://www.jstor.org/stable/10.4169/math.mag.88.5.323). See results ([below](#results)).
It held state-of-the-art results (from March 2016) until December 2019, when [researchers at Facebook](https://arxiv.org/abs/1912.02318) surpassed it by extending the idea further with explicit search.
Please feel free to contact me about Hanabi strategies, or this framework.
## Setup
Install rust (rustc and cargo), and clone this git repo.
Then, in the repo root, run `cargo run -- -h` to see usage details.
For example, to simulate a 5 player game using the cheating strategy, for seeds 0-99:
```
cargo run -- -n 100 -s 0 -p 5 -g cheat
```
Or, if the simulation is slow, build with `--release` and use more threads:
```
time cargo run --release -- -n 10000 -o 1000 -s 0 -t 4 -p 5 -g info
```
Or, to see a transcript of the game with seed 222:
```
cargo run -- -s 222 -p 5 -g info -l debug | less
```
## Strategies
To write a strategy, you simply [implement a few traits](src/strategy.rs).
The framework is designed to take advantage of Rust's ownership system
so that you *can't cheat*, without using stuff like `Cell` or `Arc` or `Mutex`.
Generally, your strategy will be passed something of type `&BorrowedGameView`.
This game view contains many useful helper functions ([see here](src/game.rs)).
If you want to mutate a view, you'll want to do something like
`let mut self.view = OwnedGameView::clone_from(borrowed_view);`.
An OwnedGameView will have the same API as a borrowed one.
Some examples:
- [Basic dummy examples](src/strategies/examples.rs)
- [A cheating strategy](src/strategies/cheating.rs), using `Rc>`
- [The information strategy](src/strategies/information.rs)!
## Results (auto-generated)
To reproduce:
```
time cargo run --release -- --results-table
```
To update this file:
```
time cargo run --release -- --write-results-table
```
On the first 20000 seeds, we have these scores and win rates (average ± standard error):
| | 2p | 3p | 4p | 5p |
|---------|------------------|------------------|------------------|------------------|
| cheat | 24.8209 ± 0.0041 | 24.9781 ± 0.0012 | 24.9734 ± 0.0014 | 24.9618 ± 0.0017 |
| | 88.40 ± 0.23 % | 98.14 ± 0.10 % | 97.83 ± 0.10 % | 97.03 ± 0.12 % |
| info | 22.5217 ± 0.0125 | 24.7946 ± 0.0039 | 24.9356 ± 0.0022 | 24.9223 ± 0.0024 |
| | 12.55 ± 0.23 % | 84.48 ± 0.26 % | 95.05 ± 0.15 % | 94.04 ± 0.17 % |
## Other work
Most similar projects I am aware of:
- https://github.com/rjtobin/HanSim (written for the paper mentioned above which introduces the information strategy)
- https://github.com/Quuxplusone/Hanabi
Some researchers are trying to solve Hanabi using machine learning techniques:
- [Initial paper](https://www.sciencedirect.com/science/article/pii/S0004370219300116) from DeepMind and Google Brain researchers. See [this Wall Street Journal coverage](https://www.wsj.com/articles/why-the-card-game-hanabi-is-the-next-big-hurdle-for-artificial-intelligence-11553875351)
- [This paper](https://arxiv.org/abs/1912.02318) from Facebook, code at https://github.com/facebookresearch/Hanabi_SPARTA which includes their machine-learned agent