from Environment.Env import Env #from Animat.Animat import Animat #from Animat.NNInitializer import NNInitializer from Animat.QLearn import QLearn import time from numpy import zeros import random #import matplotlib.pyplot as plt #import matplotlib.animation as animation print 'Running Simulation - have q learner give us an action to take' #Init Environment and food sources env = Env(50) env.makeGradient() for i in range (0,10): env.makeFoodRandom() #env.makeFood(20,20); env.updateMap() #Create Animat #a = Animat(0,0,env, filename) # This should really be inside the animat class, since that's the one that'll make # a decision on what action to take. actions = ['north','south','east','west','stay','eat','pickup','drop']; #state = getState();
import matplotlib matplotlib.use('TKAgg') import sys sys.path.append("..") from Environment.Env import Env from Animat.Animat import Animat import time from numpy import zeros import random import matplotlib.pyplot as plt import matplotlib.animation as animation print 'Running Simulation 3 - Gradient maker' mapSize = 100 food = Env(mapSize) #a = Animat.randomStart(mapSize,mapSize) food.makeGradient() print 'Made gradient.' for count in range(0,20): food.makeFoodRandom() print str(count)+' food made.' food.updateMap(); fig2 = plt.figure() plt.pcolor(food.map) plt.ion() plt.show()
import sys sys.path.append("..") from Environment.Env import Env from Animat.Animat import Animat import time from numpy import zeros import random import matplotlib.pyplot as plt import matplotlib.animation as animation print 'Running Simulation 2' mapSize = 15 food = Env(mapSize) a = Animat.randomStart(mapSize, mapSize) food.makeGradient() for iteration in range(1, 10): # Pick a random spot foody = random.randrange(0, mapSize) foodx = random.randrange(0, mapSize) print str(foody) + ' ' + str(foodx) food.map[foody, foodx] = 5 # random number # pass animat object our map # food = a.goToLocation(foody,foodx,food) # animat should behave appropriately # it should return the map unmodified # check to see if the animat did the right thing
from Environment.Env import Env from Animat.Animat import Animat from Animat.NNInitializer import NNInitializer import time from numpy import zeros import random import matplotlib.pyplot as plt import matplotlib.animation as animation if len(sys.argv) < 2: print "Filename required for neural net" exit() print 'Running Animat Simulations' #Load initial Neural Net filename = sys.argv[1] #Init Environment and food sources env = Env(250) env.makeGradient() for i in range(1, 2): env.makeFoodRandom() env.updateMap() #Create Animat a = Animat(0, 0, env, filename) while (1): a.tick()