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simulation-narrow-corridor-directed-motion-with-noise.py
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import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.offsetbox import TextArea, DrawingArea, OffsetImage, AnnotationBbox
from RL_library import return_pointwise_A
np.random.seed(1)
##### plot_two_agents #####
# plots two agents at i, j
# with s = size
# and color 1 and color 2
def plot_two_agents(i, j, zoom, filename1, filename2):
# ix, and iy are calculated by converting the point i into and x, y pair
iy = jy = 0
ix = i
jx = j
if (i == 9):
ix = 2
iy = 1
if (j == 9):
jx = 2
jy = 1
arr_lena = mpimg.imread(filename1)
imagebox = OffsetImage(arr_lena, zoom=zoom)
ab = AnnotationBbox(imagebox, (ix, iy), frameon=False)
ax = plt.axes()
ax.add_artist(ab)
arr_lena = mpimg.imread(filename2)
imagebox = OffsetImage(arr_lena, zoom=zoom)
ab = AnnotationBbox(imagebox, (jx, jy), frameon=False)
ax = plt.axes()
ax.add_artist(ab)
def one_directed_step(i, j, sim):
if (i==9):
i_in_the_hole = True
else:
i_in_the_hole = False
if (j==9):
j_in_the_hole = True
else:
j_in_the_hole = False
A = return_pointwise_A(i, j, sim)
rand = np.random.rand()
nA = np.size(A, 0)
progressive_actions = np.array([])
exist_p_m = exist_p_0 = exist_0_m = False
for fuck in range(0, nA):
if (np.array_equal(A[fuck], np.array([1, -1]))):
exist_p_m = True
if (np.array_equal(A[fuck], np.array([1, 0]))):
exist_p_0 = True
if (np.array_equal(A[fuck], np.array([0, -1]))):
exist_0_m = True
# the order of the if statements are important here.
if (exist_p_0 == True):
action = np.array([1, 0])
if (j_in_the_hole):
action = np.array([1, -7])
if (exist_0_m == True):
action = np.array([0, -1])
if (i_in_the_hole):
action = np.array([-7, -1])
if (exist_p_m == True):
action = np.array([1, -1])
if ((exist_p_0 or exist_0_m or exist_p_m)):
if (rand < 0.2):
chosen_action_id = np.random.randint(low=0, high=nA)
action = A[chosen_action_id]
else:
chosen_action_id = np.random.randint(low=0, high=nA)
action = A[chosen_action_id]
print("action")
print(action)
iprime, jprime = np.array([i, j]) + action
if (iprime == jprime):
iprime = i
jprime = j
print("iprime = "+str(iprime)+" jprime = "+str(jprime))
return iprime, jprime
#### The main program #####
#### 1. initialization #####
# 1.1 parameters
n = 10
sim = True # True is simultaneous motion of particles are allowed
# 1.2 initial position of the agents
i = 0
j = 8
# 1.3 plotting the grid
plt.close('all')
plt.ion()
ax = plt.axes()
ax.axis('off')
figure = plt.gcf()
figure.set_size_inches(13, 5)
boxsize = 2700
for x in range(1,8):
plt.scatter(x, 0, s=boxsize, c='white', marker='s',linewidths=1, edgecolor='black' )
plt.scatter(0, 0, s=boxsize, c='white', marker='s',linewidths=2, edgecolor='red' )
plt.scatter(8, 0, s=boxsize, c='white', marker='s',linewidths=2, edgecolor='blue' )
plt.scatter(2, 1, s=boxsize, c='white', marker='s',linewidths=1, edgecolor='black' )
#plt.scatter(i, 0, s=100, c='red')
#plt.scatter(j, 0, s=100, c='blue')
plt.axis([-2, n+1, -2, 3])
plt.axes().set_aspect('equal')
plt.scatter(i, j, c='red')
la_linea_left = './la_linea/la_linea_red_walking_left.png'
la_linea_right = './la_linea/la_linea_blue_walking_right.png'
zoom = 0.11
#### 2. simulation ####
step = 0
while (i!=8 or j!=0 and step<1):
# 2.1 one step following the policy
iprime, jprime = one_directed_step(i, j, sim)
# 2.2 checking if they have arrived
# if they arrive they dont leave anymore; not a major intervation
if (j == 0):
jprime = 0
if (i == 8):
iprime = 8
if (jprime == 0):
la_linea_left = './la_linea/la_linea_happy_red.png'
if (iprime == 8):
la_linea_right = './la_linea/la_linea_happy_blue.png'
# 2.2 checking if they have arrived
#if they arrive they dont leave anymore; not a major intervation
if (j == 0):
jprime = 0
la_linea_left = './la_linea/la_linea_happy_red.png'
if (i == 8):
iprime = 8
la_linea_right = './la_linea/la_linea_happy_blue.png'
# 2.3 plotting
# plot_two_agents(i, j, 100, '#C1C7C9', '#C1C7C9') # plotting gray traces of the agents
plot_two_agents(i, j, zoom*1.05, './la_linea/white.png', './la_linea/white.png') #
plot_two_agents(iprime, jprime, zoom, la_linea_right, la_linea_left) # plotting the agents in their new states
plt.pause(0.1)
# 2.4 updating the position of the agents
i = iprime
j = jprime
if (step < 10):
filename = 'state_00'+str(step)+'.png'
elif (step < 100):
filename = 'state_0'+str(step)+'.png'
elif (step < 1000):
filename = 'state_'+str(step)+'.png'
plt.savefig(filename)
step += 1