051ac7a097
Suppose we have `total_information` choices, and we first use them to encode the answer `x` to a question with `m` answers. That answer is encoded by the choice we take modulo `m`. How much "information" do we have left? That depends on the number of numbers less than `total_information` that are equal to `x` modulo `m`. Depending on the value of `x`, this is either `floor(total_information/m)` or `floor(total_information/m) + 1`. We now use all of this information as opposed to just `floor(total_information/m)`, at the cost of making our math not a lot more complicated but pretty confusing.
2.7 KiB
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. It also explores some implementations, based on ideas from this paper. In particular, it contains an improved version of their "information strategy", which achieves the best results I'm aware of for games with more than 2 players (see below).
Please feel free to contact me about Hanabi strategies, or this framework.
Most similar projects I am aware of:
- https://github.com/rjtobin/HanSim (written for the paper mentioned above)
- https://github.com/Quuxplusone/Hanabi
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:
- Basic dummy examples
- A cheating strategy, using
Rc<RefCell<_>> - The information strategy!
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 average scores and win rates:
| 2p | 3p | 4p | 5p | |
|---|---|---|---|---|
| cheat | 24.8594 | 24.9785 | 24.9720 | 24.9557 |
| 90.59 % | 98.17 % | 97.76 % | 96.42 % | |
| info | 22.3736 | 24.7840 | 24.9261 | 24.9160 |
| 10.41 % | 84.14 % | 94.33 % | 93.49 % |