83 lines
3.6 KiB
Markdown
83 lines
3.6 KiB
Markdown
# Simulations of Hanabi strategies
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Hanabi is a cooperative card game of incomplete information.
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Despite relatively [simple rules](https://boardgamegeek.com/article/10670613#10670613),
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the space of Hanabi strategies is quite interesting.
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This project provides a framework for implementing Hanabi strategies in Rust, and also implements extremely strong strategies.
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The best strategy is based on the "information strategy" from
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[this paper](https://d0474d97-a-62cb3a1a-s-sites.googlegroups.com/site/rmgpgrwc/research-papers/Hanabi_final.pdf). See results ([below](#results)).
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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.
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Please feel free to contact me about Hanabi strategies, or this framework.
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## Setup
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Install rust (rustc and cargo), and clone this git repo.
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Then, in the repo root, run `cargo run -- -h` to see usage details.
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For example, to simulate a 5 player game using the cheating strategy, for seeds 0-99:
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```
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cargo run -- -n 100 -s 0 -p 5 -g cheat
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```
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Or, if the simulation is slow, build with `--release` and use more threads:
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```
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time cargo run --release -- -n 10000 -o 1000 -s 0 -t 4 -p 5 -g info
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```
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Or, to see a transcript of the game with seed 222:
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```
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cargo run -- -s 222 -p 5 -g info -l debug | less
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```
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## Strategies
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To write a strategy, you simply [implement a few traits](src/strategy.rs).
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The framework is designed to take advantage of Rust's ownership system
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so that you *can't cheat*, without using stuff like `Cell` or `Arc` or `Mutex`.
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Generally, your strategy will be passed something of type `&BorrowedGameView`.
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This game view contains many useful helper functions ([see here](src/game.rs)).
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If you want to mutate a view, you'll want to do something like
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`let mut self.view = OwnedGameView::clone_from(borrowed_view);`.
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An OwnedGameView will have the same API as a borrowed one.
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Some examples:
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- [Basic dummy examples](src/strategies/examples.rs)
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- [A cheating strategy](src/strategies/cheating.rs), using `Rc<RefCell<_>>`
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- [The information strategy](src/strategies/information.rs)!
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## Results (auto-generated)
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To reproduce:
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```
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time cargo run --release -- --results-table
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```
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To update this file:
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```
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time cargo run --release -- --write-results-table
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```
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On the first 20000 seeds, we have these scores and win rates (average ± standard error):
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| | 2p | 3p | 4p | 5p |
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|---------|------------------|------------------|------------------|------------------|
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| cheat | 24.8594 ± 0.0036 | 24.9785 ± 0.0012 | 24.9720 ± 0.0014 | 24.9557 ± 0.0018 |
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| | 90.59 ± 0.21 % | 98.17 ± 0.09 % | 97.76 ± 0.10 % | 96.42 ± 0.13 % |
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| info | 22.5218 ± 0.0125 | 24.7942 ± 0.0039 | 24.9352 ± 0.0022 | 24.9224 ± 0.0024 |
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| | 12.55 ± 0.23 % | 84.44 ± 0.26 % | 94.99 ± 0.15 % | 94.04 ± 0.17 % |
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## Other work
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Most similar projects I am aware of:
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- https://github.com/rjtobin/HanSim (written for the paper mentioned above which introduces the information strategy)
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- https://github.com/Quuxplusone/Hanabi
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Some researchers are trying to solve Hanabi using machine learning techniques:
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- [Initial paper](https://arxiv.org/abs/1902.00506) 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)
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- [This paper](https://arxiv.org/abs/1912.02318) from Facebook, code at https://github.com/facebookresearch/Hanabi_SPARTA which includes their machine-learned agent
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