[0, 1, 0, 0, 0, 0],

[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles

[0, 1, 0, 0, 0, 0],

[0, 1, 0, 0, 1, 0],

[0, 0, 0, 0, 1, 0],

closed = [

[0 for col in range(len(grid[0]))] for row in range(len(grid))

] # the reference grid

closed[init[0]][init[1]] = 1

action = [

[0 for col in range(len(grid[0]))] for row in range(len(grid))

] # the action grid

x = init[0]

y = init[1]

g = 0

f = g + heuristic[init[0]][init[0]]

cell = [[f, g, x, y]]

found = False # flag that is set when search is complete

resign = False # flag set if we can't find expand

while not found and not resign:

if len(cell) == 0:

return "FAIL"

else: # to choose the least costliest action so as to move closer to the goal

cell.sort()

cell.reverse()

next = cell.pop()

x = next[2]

y = next[3]

g = next[1]

if x == goal[0] and y == goal[1]:

found = True

else:

for i in range(len(delta)): # to try out different valid actions

x2 = x + delta[i][0]

y2 = y + delta[i][1]

if x2 >= 0 and x2 < len(grid) and y2 >= 0 and y2 < len(grid[0]):

if closed[x2][y2] == 0 and grid[x2][y2] == 0:

g2 = g + cost

f2 = g2 + heuristic[x2][y2]

cell.append([f2, g2, x2, y2])

closed[x2][y2] = 1

action[x2][y2] = i

invpath = []

x = goal[0]

y = goal[1]

invpath.append([x, y]) # we get the reverse path from here

while x != init[0] or y != init[1]:

x2 = x - delta[action[x][y]][0]

y2 = y - delta[action[x][y]][1]

x = x2

y = y2

invpath.append([x, y])

path = []

for i in range(len(invpath)):

path.append(invpath[len(invpath) - 1 - i])

print("ACTION MAP")

for i in range(len(action)):

print(action[i])