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)]; toFollow = [0 if toEat == 1 else 1,0 if toEat == 1 else 1]; for i in range(0,1000): env1.tick() env2.tick() for index,a in enumerate(animats): if stateMachine[index] == 'fail': toEat[index] = random.randrange(0,2); toFollow[index] = 0 if toEat == 1 else 1;
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))
Animat.foodTargeting = False
Animat.allowDeath = False
trainingTicks = 10000
# Training session
for i in range(0, trainingTicks):
for e in env:
e.tick()
Animat.startTick()
for a in animats:
stillAlive = a.tick()
if not stillAlive:
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
# check to see if the animat did the right thing
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)); Animat.allowDeath = False trainingTicks = 7000 # Training session for i in range(0,trainingTicks): for e in env: e.tick() Animat.startTick() for a in animats: stillAlive = a.tick() if not stillAlive: print a.energy for e in env: #e.map[a.y,a.x] = e.map.max() if e.map.max() > 0 else 1; e.binaryGradient[a.y,a.x] = e.binaryGradient.max() if e.binaryGradient.max() > 0 else 1; #e.simpleMap[a.y,a.x] = e.simpleMap.max() if e.simpleMap.max() > 0 else 1; #e.simpleMap[a.y,a.x] = 10; #toPlot[a.y,a.x] = 5; #print "e.simpleMap.max() is :"+str(e.simpleMap.max()); Animat.endTick() if i % 100 == 0: #for e in env: #im = plt.imshow(env[0].map+env[1].map)
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()
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:
a.tickStateMachine()
env.map[a.y, a.x] = env.map.max()
im = plt.imshow(env.map)
im.set_cmap('spectral')
ims.append([im])
env.tick()
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
if env.canMove(a.y, a.x, a.y - 1, a.x):
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()
ims = []
for i in range(0, 50000):
for e in env:
e.tick()
for a in animats:
a.tick()
for e in env:
e.map[a.y, a.x] = e.map.max()
if i % 100 == 0:
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()
#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 = []; numAnimats = 20; for a in range(0,numAnimats): animats.append(Animat(random.randrange(0,mapsize),random.randrange(0,mapsize),env,foodTypes)); Animat.allowDeath = False; fig = plt.figure() ims = [] toPlot = zeros((mapsize,mapsize)); # Training session for i in range(0,15000): for e in env: e.tick() Animat.startTick() for a in animats: a.tick() for e in env: e.binaryGradient[a.y,a.x] = e.binaryGradient.max() if e.binaryGradient.max() > 0 else 1;
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 = []
numAnimats = 20
for a in range(0, numAnimats):
animats.append(
Animat(random.randrange(0, mapsize), random.randrange(0, mapsize), env,
foodTypes))
Animat.allowDeath = True
fig = plt.figure()
ims = []
toPlot = zeros((mapsize, mapsize))
# Training session
for i in range(0, 7000):
for e in env:
e.tick()
for a in animats:
a.tick()
for e in env:
e.binaryGradient[a.y, a.x] = e.binaryGradient.max(
) if e.binaryGradient.max() > 0 else 1
]
for e in env:
e.makeGradient()
for i in range(0, 100):
e.makeFoodRandom()
e.updateMap()
fig = plt.figure()
ims = []
toPlot = zeros((mapsize, mapsize))
animats = []
deadAnimats = []
for a in range(0, 20):
animats.append(
Animat(random.randrange(0, mapsize), random.randrange(0, mapsize), env,
foodTypes, 1000, 500))
Animat.allowDeath = False
# Training session
for i in range(0, 9000):
for e in env:
e.tick()
for a in animats:
isAlive = a.tick()
if not isAlive:
animats.remove(a)
deadAnimats.append(a)
if len(animats) <= 0:
print "All animats dead"
break
#for e in env:
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:
a.tickStateMachine()
env.map[a.y,a.x] = env.map.max();
im = plt.imshow(env.map)
im.set_cmap('spectral')
ims.append([im])
env.tick()
