#import the RL libraries

from scipy import *

import sys, time,pickle

from PygameEnvironment import TwentyFortyEightEnvironment

from pybrain.tools.shortcuts import buildNetwork

from pybrain.optimization import GA #HillClimber

from pybrain.rl.agents import OptimizationAgent

from pybrain.rl.experiments import EpisodicExperiment

from pybrain.rl.environments import Task

#set the learning time

learning_eps = 150

#set the batch size

games_per_ep = 75

population_size = 150

# make the environment

environment = TwentyFortyEightEnvironment()

environment.meansize = 1.0/(population_size*games_per_ep)

#the task is the game this time

task = environment

#create our network

controller = buildNetwork(task.nsenses, 20, task.nactions)

#use a Genetic Algorithm

#all the commented out lines are options you can play with

learner = GA(populationSize=population_size

)

agent = OptimizationAgent(controller, learner)

#set up an experiment

experiment = EpisodicExperiment(task, agent)

meanscores = []

for i in xrange(learning_eps):

print i

experiment.doEpisodes(games_per_ep)

meanscores.append(learner.bestEvaluation)

f = open("bestEvo.pkl",'w')

pickle.dump(learner.bestEvaluable,f)

f.close()

import matplotlib.pyplot as plt

plt.plot(meanscores)

plt.title("Best Agent Score vs Generations:")

plt.show()