# go-cfr

**Repository Path**: gc063tzf/go-cfr

## Basic Information

- **Project Name**: go-cfr
- **Description**: go-cfr implements several forms of Counter Factual Regret minimization in Golang
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-11-01
- **Last Updated**: 2020-12-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

go-cfr
======

go-cfr is a package that implements several forms of [Counterfactual Regret Minimization](https://www.quora.com/What-is-an-intuitive-explanation-of-counterfactual-regret-minimization) in [Go](https://golang.org).
CFR can be used to solve for an approximate [Nash Equilibrium](https://en.wikipedia.org/wiki/Nash_equilibrium)
in an imperfect information [extensive-form game](https://en.wikipedia.org/wiki/Extensive-form_game).

This project is a research library used to study different forms of CFR. For a similar alternative in C++/Python/Swift, see [OpenSpiel](https://github.com/deepmind/open_spiel).

[![No Maintenance Intended](http://unmaintained.tech/badge.svg)](http://unmaintained.tech/)
[![GoDoc](https://godoc.org/github.com/timpalpant/go-cfr?status.svg)](http://godoc.org/github.com/timpalpant/go-cfr)
[![Build Status](https://travis-ci.org/timpalpant/go-cfr.svg?branch=master)](https://travis-ci.org/timpalpant/go-cfr)
[![Coverage Status](https://coveralls.io/repos/timpalpant/go-cfr/badge.svg?branch=master&service=github)](https://coveralls.io/github/timpalpant/go-cfr?branch=master)
[![Go Report Card](https://goreportcard.com/badge/github.com/timpalpant/go-cfr)](https://goreportcard.com/badge/github.com/timpalpant/go-cfr)

## Usage

To use CFR, you must implement the extensive-form game tree for your game,
by implementing the `GameTreeNode` interface.

An implementation of [Kuhn Poker](https://en.wikipedia.org/wiki/Kuhn_poker) is included
as an example.

```Go
package main

import (
	"fmt"

	"github.com/timpalpant/go-cfr"
	"github.com/timpalpant/go-cfr/kuhn"
	"github.com/timpalpant/go-cfr/tree"
)

func main() {
	poker := kuhn.NewGame()
	vanillaCFR := cfr.NewVanilla()
	nIter := 10000
	expectedValue := 0.0
	for i := 1; i <= nIter; i++ {
		expectedValue += vanillaCFR.Run(poker)
	}

	expectedValue /= float64(nIter)
	fmt.Printf("Expected value is: %v\n", expectedValue)

	tree.VisitInfoSets(poker, func(player int, infoSet string) {
		strat := vanillaCFR.GetStrategy(player, infoSet)
		if strat != nil {
			fmt.Printf("[player %d] %6s: check=%.2f bet=%.2f\n",
				player, infoSet, strat[0], strat[1])
		}
	})
}
```

## Variants implemented

- Vanilla CFR: https://poker.cs.ualberta.ca/publications/NIPS07-cfr.pdf
- CFR+: https://arxiv.org/abs/1407.5042
- Discounted (including Linear) CFR: https://arxiv.org/abs/1809.04040
- Monte Carlo CFR (MC-CFR):
    - Chance Sampling, External Sampling, Outcome Sampling CFR: http://mlanctot.info/files/papers/nips09mccfr.pdf
    - Average Strategy CFR: https://papers.nips.cc/paper/4569-efficient-monte-carlo-counterfactual-regret-minimization-in-games-with-many-player-actions.pdf
    - Robust Sampling CFR: https://arxiv.org/abs/1901.07621
    - Generalized Sampling CFR: https://dl.acm.org/citation.cfm?id=2900920
- Deep CFR: https://arxiv.org/abs/1811.00164
- Single Deep CFR: https://arxiv.org/abs/1901.07621

## License

go-cfr is released under the [GNU Lesser General Public License, Version 3.0](https://www.gnu.org/licenses/lgpl-3.0.en.html).