10 releases
0.4.2 | Nov 20, 2023 |
---|---|
0.4.1 | Jul 26, 2023 |
0.4.0 | Jan 15, 2023 |
0.3.1 | Oct 22, 2022 |
0.0.1 | Jul 3, 2022 |
#29 in Games
42 downloads per month
175KB
4K
SLoC
Counterfactual Regret (CFR)
Counterfactual regret minimization solver for two-player zero-sum incomplete-information games in rust. This is a rust library and binary for computing approximate nash-equilibria of two-player zero-sum incomplete-information games.
Usage
Library
To use the library part of this crate, add the following to your Cargo.toml
.
[dependencies]
cfr = { version = "0.1.0", default-features = false }
Then implement
IntoGameNode
, for
a type that represents a node in your game tree (or alternatively can generate
new nodes).
Finally execute:
use cfr::{Game, IntoGameNode};
struct MyData { ... }
impl IntoGameNode for MyData { ... }
let game = Game::from_root(...)?;
let strategies = game.solve_external();
let info = strategies.get_info();
let regret = info.regret();
let [player_one, player_two] = strategies.as_named();
Binary
This package can also be used as a binary with a few different input formats. install it with
$ cargo install cfr
Solving a game will produce the expected utility to player one, as well as the regrets and the strategy found in json format.
$ cfr -i game_file
{
"expected_one_utility": 0.05555555727916178,
"player_one_regret": 1.186075528125663e-06,
"player_two_regret": 8.061797025377127e-05,
"regret": 8.061797025377127e-05,
"player_one_strategy": { ... },
"player_two_regret": { ... }
}
The command line tool can interpret a custom json dsl, and
gambit .efg
files.
JSON Format
The DSL is defined by:
node ::= terminal || chance || player
terminal ::= { "terminal": <number> }
chance ::= {
"chance": {
"infoset"?: <string>,
"outcomes": {
"<named outcome>": { "prob": <number>, "state": node },
...
}
}
}
player ::= {
"player": {
"player_one": <bool>,
"infoset": <string>,
"actions": { "<named action>": node, ... }
}
}
A minimal example highlighting all types of nodes, but of an uninteresting game is:
{
"chance": {
"outcomes": {
"single": {
"prob": 1.0,
"state": {
"player": {
"player_one": true,
"infoset": "none",
"actions": {
"only": {
"terminal": 0.0
}
}
}
}
}
}
}
}
In this game there's a 100% chance of the "single"
outcome, followed by a
move by player one where they have information "none"
and only have one
action: "only"
. After selecting that action, they get payoff 0.
Gambit Format
The gambit format follows the standard gambit extensive form game format, with some mild restrictions.
- Gambit specifies that actions are optional, but this requires every player and chance node specifies thier actions.
- Actions must be unique, and there are some very mild restrictions on non-conflicting information set names (See duplicate infosets).
- The gambit format allows for arbitrary player, non-constant-sum extensive form games, but this only allows two-player constant-sum perfect recall games.
- For efficiency this uses double precision floats, because equilibria are approximate, thus in extreme circumstances payoffs might not be representable.
Examples
If implementing IntoGameNode is confusing for your game, here are more complicated examples of games that don't quite fit in the documentation:
- Kuhn Poker - this
can also be run using
cargo run --example kuhn_poker --
. - Liar's Dice - this is implemented as a benchmark to allow using nightly apis.
Errors
This section has more details on errors the command line might return.
Json Error
This error occurs when the json doesn't match the expected format for reading. See JSON Format for details on the specification, and make sure that the json you're providing matches that specification.
Gambit Error
This error occurs when the gambit file can't be parsed. There should be more info about exactly where the error occured. See gambit format for more details on the format.
Auto Error
The game file couldn't be parsed by any known game format. In order to get more
detailed errors regarding the parsing failure, try rerunning again with
--input-format <format>
to get more precise errors
Duplicate Infosets
Gambit .efg
files don't require naming infosets, but cfr
requires string
names for every infoset. It will default to useing the string version of the
infoset number, but this will fail if that infoset name is already taken. For
example:
...
p 1 1 "2" { ... } 0
p 1 2 { ... } 0
...
will throw this error as long as a name for infoset 2 isn't defined elsewhere.
Constant Sum
Counterfactual regret minimization only works on constant sum games. Since
gambit files define payoffs independently, this verifies that the range of the
sum of every profile is less than 0.1% of the range of the of the payoffs for a
single player. If you encounter this error, cfr
will not for this game.
Game Error
This error occurs if there were problems with the game specification that made
creating a compact game impossible. See the documentation of
GameError
for more
details.
Solve Error
The error occured if there were problems solving it. This should only happen if
you requested too many threads, or the threads couldn't be spawned. See the
documentation of
SolveError
for more
details.
To Do
- With the implementation of discounted CFR, the incremental regret updates hold even less frequrntly. We should instead check the true regret after a certain number of iterations. This takes some restructuing, as well as some benchmarking to make sure that time isn't wasted on regret calculation.
- We currently set the number of threads arbitrarily high, but we have the full game tree before we decide how many threads to spawn, so we could be smarter about both setting a maximum, and setting the load factor based on the tree size.
- This currently requires that infosets and actions be hashable and,
depending on the usecase, cloneable. Most of the functions could be rewritten
to take combinations of
Hash
,Ord
, orClone
and use the most performant option given the trait bounds. This doesn't seem particularly useful and would be a lot of work, so it's omitted for now.
Dependencies
~4–13MB
~169K SLoC