To help you get started, we've collected the most common ways that Axelrod is being used within popular public projects.

Axelrod-Python / Axelrod / axelrod / strategies / punisher.py View on Github

self.grudge_memory = 0
            self.grudged = False

        if self.grudged:
            self.grudge_memory += 1
            return D

        elif D in opponent.history[-1:]:
            self.mem_length = (opponent.defections * 20) // len(opponent.history)
            self.grudged = True
            return D

        return C


class InversePunisher(Player):
    """
    An inverted version of Punisher. The player starts by cooperating however
    will defect if at any point the opponent has defected, and forgets after
    mem_length matches, with 1 <= mem_length <= 20. This time mem_length is
    proportional to the amount of time the opponent has played C.

    Names:

    - Inverse Punisher: Original name by Geraint Palmer
    """

    name = "Inverse Punisher"
    classifier = {
        "memory_depth": float("inf"),  # Long memory
        "stochastic": False,
        "makes_use_of": set(),

View more ways to use Axelrod

Axelrod

Goals

A Python library with the following principles and goals:

1. Enabling the reproduction of previous Iterated Prisoner's Dilemma research as easily as possible.
2. Creating the de-facto tool for future Iterated Prisoner's Dilemma research.
3. Providing as simple a means as possible for anyone to define and contribute new and original Iterated Prisoner's Dilemma strategies.
4. Emphasizing readability along with an open and welcoming community that is accommodating for developers and researchers of a variety of skill levels.

Features

With Axelrod you:

• have access to over 200 strategies, including original and classics like Tit For Tat and Win Stay Lose Shift. These are extendable through parametrization and a collection of strategy transformers.

• can create head to head matches between pairs of strategies.

• can create tournaments over a number of strategies.

• can study population dynamics through Moran processes and an infinite population model.

• can analyse detailed results of tournaments and matches.

• can visualise results of tournaments.

  

• can reproduce a number of contemporary research topics such as fingerprinting of strategies and morality metrics.

  

The library has 100% test coverage and is extensively documented. See the documentation for details and examples of all the features:

An open reproducible framework for the study of the iterated prisoner's dilemma: a peer reviewed paper introducing the library (22 authors).

Installation

The library is tested on Python versions 3.8, 3.9, and 3.10.

The simplest way to install is:

$ pip install axelrod

To install from source:

$ git clone https://github.com/Axelrod-Python/Axelrod.git
$ cd Axelrod
$ python setup.py install

Quick Start

The following runs a basic tournament:

>>> import axelrod as axl
>>> players = [s() for s in axl.demo_strategies]  # Create players
>>> tournament = axl.Tournament(players, seed=1)  # Create a tournament
>>> results = tournament.play()  # Play the tournament
>>> results.ranked_names
['Defector', 'Grudger', 'Tit For Tat', 'Cooperator', 'Random: 0.5']

Examples

• is a tournament pitting all the strategies in the repository against each other. These results can be easily viewed at .
• contains a set of example Jupyter notebooks.
• contains fingerprints (data and plots) of all strategies in the library.

Contributing

All contributions are welcome!

You can find helpful instructions about contributing in the documentation:

Publications

You can find a list of publications that make use of or cite the library on the citations page.

Contributors

The library has had many awesome contributions from many great contributors. The Core developers of the project are:

• drvinceknight
• gaffney2010
• marcharper
• meatballs
• nikoleta-v3