stagHunt

stagHunt.py

import numpy
import random

class Agent():
    def __init__(self,
                 location={'x': 0, 'y': 0},
                 vision=1,
                 strategy='defect',
                 fitness=0,
                 EndofTheWorld=32):
        self.location = location
        self.vision = vision
        self.strategy = strategy
        self.fitness = fitness
        self.sexyness = 0

        self.neighborhood = [{'x': i, 'y': j}
                             for i in range(self.location['x'] - self.vision,
                                            self.location['x'] + self.vision + 1)
                             if i >= 0 and i <= EndofTheWorld
                             for j in range(self.location['y'] - self.vision,
                                            self.location['y'] + self.vision + 1)
                             if j >= 0 and j <= EndofTheWorld]

    def __str__(self):
        return 'Strategy: ' + str(self.strategy)

    def moore_agents(self, agents):
        return [agent for agent in agents if
                agent.location in self.neighborhood and agent != self]

    def play(self, agents, pay_offs):
        self.sexyness = numpy.sum([pay_offs[self.strategy + '-' + agent.strategy]
                                   for agent in self.moore_agents(agents)])
        self.fitness += self.sexyness

    def learn(self, agents):
        agents_to_eyeup = self.moore_agents(agents)
        sexiest_agents = [a for a in agents_to_eyeup
                          if a.sexyness == max([b.sexyness for b in agents_to_eyeup])]
        agent_to_consider_copying = random.choice(sexiest_agents)
        if agent_to_consider_copying.sexyness > self.sexyness:
            self.strategy = agent_to_consider_copying.strategy

pay_offs = {'cooperate-cooperate': 4,
            'cooperate-defect': 0,
            'defect-cooperate': 3,
            'defect-defect': 1}

a = Agent(location={'x': 1, 'y': 1})
b = Agent(location={'x': 2, 'y': 1}, strategy='cooperate')

agents = [a,b]

print b.fitness, b.strategy
a.play(agents,pay_offs)
b.play(agents,pay_offs)
b.learn(agents)
print b.fitness, b.strategy
b.play(agents,pay_offs)
print b.fitness, b.strategy