import time from numpy import zeros import random import matplotlib.pyplot as plt import matplotlib.animation as animation from pylab import figure from DataAnalysis.DataAnalysis import Plotter print 'Running Simulation - Add food generators to corners' testname = "gen_corners" #Init Environment and food sources foodTypes = [0,1,2,3]; mapsize = 100; env = [Env(mapsize,foodTypes[0]),Env(mapsize,foodTypes[1]), Env(mapsize,foodTypes[2]),Env(mapsize,foodTypes[3])]; for e in env: e.makeGradient() for i in range (0,100): e.makeFoodRandom() e.updateMap() animats = []; for a in range(0,20): animats.append(Animat(random.randrange(0,mapsize),random.randrange(0,mapsize),env,foodTypes,1500,1000)); #fig = plt.figure() #ims = [] #toPlot = zeros((mapsize,mapsize));
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 print 'Running Simulation - Moving food around, picking up, dropping' # Deprecated filename = 'nn_precise_100k.p' size = 50; env1 = Env(size,0) env2 = Env(size,1); for i in range (0,20): env1.makeFoodRandom(); env2.makeFoodRandom(); env1.updateMap() env2.updateMap() animats = [Animat(25,25,[env1,env2],filename),Animat(10,40,[env1,env2],filename)]; stateMachine = ['notholding','notholding']; fig = plt.figure() ims = [] toEat = [random.randrange(0,2),random.randrange(0,2)];
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 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 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
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()
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 print 'Running Simulation - Find food' #filename = 'nn_scents_based.p' filename = 'nn_precise_100k.p' #Init Environment and food sources env = Env(50) for i in range(0, 10): env.makeFoodRandom() #env.makeFood(20,20); env.updateMap() #Create Animat ID = 1 a = Animat(0, 0, env, filename, 1) fig = plt.figure() ims = [] for i in range(0, 1000): print "Tick: " + str(i) env.tick() if a.alive:
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 print 'Running Simulation - Find food' #filename = 'nn_scents_based.p' filename = 'nn_precise_100k.p' #Init Environment and food sources env = [Env(50, 0), Env(50, 1)] for e in env: e.makeGradient() for i in range(0, 100): e.makeFoodRandom() e.updateMap() #Create Animat animats = [ Animat(25, 25, env, filename), Animat(10, 40, env, filename), Animat(45, 10, env, filename), Animat(30, 40, env, filename) ] fig = plt.figure()
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 = 13 food = Env(mapSize) a = Animat.randomStart(mapSize,mapSize) food.makeGradient() fig1 = plt.figure() plt.pcolor(food.gradient) plt.ion() plt.show() food.makeFoodRandom() fig2 = plt.figure() plt.pcolor(food.map) plt.ion() plt.show()
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 1" mapSize = 20 env = Env(mapSize) a = Animat.randomStart(mapSize, mapSize) # Create figure for plotting our environment fig = plt.figure() ims = [] # Walk around the map randomly, for 100 iterations for t in range(1, 100): # time.sleep(.05) # print 'Time: '+str(t) # Pick a random spot to move to randomSpot = random.randrange(1, 4 + 1) if randomSpot == 1: # north
import sys sys.path.append("..") from Environment.Env import Env import matplotlib.pyplot as plt from random import choice import cPickle as pickle import time print 'Running Simulation - Make scents' #Init Environment and food sources envSize = 1000 env = Env(envSize) env.makeGradient() for i in range(0, 70): env.makeFoodRandom() env.updateMap() fig = plt.figure() im = plt.imshow(env.map) im.set_cmap('spectral') plt.ion() plt.show() print 'Making training data...' t0 = time.clock() # Traverse inputTraining = []
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 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 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
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 1' mapSize = 20 env = Env(mapSize) a = Animat.randomStart(mapSize,mapSize) # Create figure for plotting our environment fig = plt.figure() ims = [] # Walk around the map randomly, for 100 iterations for t in range(1,100): #time.sleep(.05) #print 'Time: '+str(t) # Pick a random spot to move to randomSpot = random.randrange(1,4+1) if randomSpot == 1: # north
from numpy import zeros import random import matplotlib.pyplot as plt import matplotlib.animation as animation from pylab import figure from DataAnalysis.DataAnalysis import Plotter print 'Running Simulation - Add food generators to corners' testname = "gen_corners" #Init Environment and food sources foodTypes = [0, 1, 2, 3] mapsize = 100 env = [ Env(mapsize, foodTypes[0]), Env(mapsize, foodTypes[1]), Env(mapsize, foodTypes[2]), Env(mapsize, foodTypes[3]) ] for e in env: e.makeGradient() for i in range(0, 100): e.makeFoodRandom() e.updateMap() animats = [] for a in range(0, 20): animats.append( Animat(random.randrange(0, mapsize), random.randrange(0, mapsize), env,
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 4 - Food generator test' mapSize = 100 food = Env(mapSize) #a = Animat.randomStart(mapSize,mapSize) food.makeGradient() print 'Made gradient.' food.addFoodGenerator(20,20,10) food.addFoodGenerator(90,50,50) food.addFoodGenerator(25,88,200) ims = [] fig = plt.figure() for iterations in range(0,1000): food.tick() # animation im = plt.imshow(food.map) ims.append([im])
sys.path.append("..") 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();
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 print 'Running Simulation - Find food' #filename = 'nn_scents_based.p' filename = 'nn_precise_100k.p' #Init Environment and food sources env = Env(50) for i in range (0,10): env.makeFoodRandom() #env.makeFood(20,20); env.updateMap() #Create Animat ID = 1; a = Animat(0,0,env, filename,1) fig = plt.figure() ims = [] for i in range(0,1000): print "Tick: "+str(i); env.tick() if a.alive: