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Simulations of Hanabi strategies

Hanabi is a cooperative card game of incomplete information. Despite relatively simple rules, 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. See results (below). It held state-of-the-art results (from March 2016) until December 2019, when researchers at Facebook 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.

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). 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:

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:

Some researchers are trying to solve Hanabi using machine learning techniques:

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State of the art Hanabi bots + simulation framework in rust

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