Welcome to the documentation for the Axelrod Python library¶

Here is quick overview of the current capabilities of the library:

• Over 100 strategies from the literature and some exciting original contributions

• Classic strategies like TiT-For-Tat, WSLS, and variants
• Zero-Determinant and other Memory-One strategies
• Many generic strategies that can be used to define an array of popular strategies, including finite state machines, strategies that hunt for patterns in other strategies, and strategies that combine the effects of many others
• Strategy transformers that augment the abilities of any strategy

• Round Robin tournaments with a variety of options, including:

• noisy environments
• spatial tournaments
• probabilistically chosen match lengths
• Population dynamics

• The Moran process
• An ecological model
• Multi-processor support (not currently supported on Windows), caching for deterministic interactions, automatically generate figures and statistics

Every strategy is categorized on a number of dimensions, including:

• Deterministic or Stochastic
• How many rounds of history used
• Whether the strategy makes use of the game matrix, the length of the match, etc.

Furthermore the library is extensively tested with 100% coverage, ensuring validity and reproducibility of results!

Quick start¶

Create matches between two players:

```>>> import axelrod as axl
>>> players = (axl.Alternator(), axl.TitForTat())
>>> match = axl.Match(players, 5)
>>> interactions = match.play()
>>> interactions
[(C, C), (D, C), (C, D), (D, C), (C, D)]
```

Build full tournaments between groups of players:

```>>> import axelrod as axl
>>> players = (axl.Cooperator(), axl.Alternator(), axl.TitForTat())
>>> tournament = axl.Tournament(players)
>>> results = tournament.play()
>>> results.ranked_names
['Alternator', 'Tit For Tat', 'Cooperator']
```

Study the evolutionary process using a Moran process:

```>>> import axelrod as axl
>>> players = (axl.Cooperator(), axl.Alternator(), axl.TitForTat())
>>> mp = axl.MoranProcess(players)
>>> populations = mp.play()
>>> populations
[Counter({'Alternator': 1, 'Cooperator': 1, 'Tit For Tat': 1}),
Counter({'Alternator': 1, 'Cooperator': 1, 'Tit For Tat': 1}),
Counter({'Cooperator': 1, 'Tit For Tat': 2}),
Counter({'Cooperator': 1, 'Tit For Tat': 2}),
Counter({'Tit For Tat': 3})]
```

As well as this, the library has a growing collection of strategies. The Strategies index gives a description of them.

For further details there is a library of Tutorials available and a Community page with information about how to get support and/or make contributions.