astar + graph search
This commit is contained in:
Aliaksei Brown
parent
1a0e89c1e7
commit
be0f333181
BIN
agent/methods/__pycache__/a_star.cpython-310.pyc
Normal file
BIN
agent/methods/__pycache__/a_star.cpython-310.pyc
Normal file
Binary file not shown.
136
agent/methods/a_star.py
Normal file
136
agent/methods/a_star.py
Normal file
@ -0,0 +1,136 @@
|
||||
class Node:
|
||||
def __init__(self, state, parent='', action='', distance=0):
|
||||
self.state = state
|
||||
self.parent = parent
|
||||
self.action = action
|
||||
self.distance = distance
|
||||
|
||||
class Search:
|
||||
def __init__(self, cell_size, cell_number):
|
||||
self.cell_size = cell_size
|
||||
self.cell_number = cell_number
|
||||
|
||||
def succ(self, state):
|
||||
x = state[0]
|
||||
y = state[1]
|
||||
angle = state[2]
|
||||
match(angle):
|
||||
case 'UP':
|
||||
possible = [['left', x, y, 'LEFT'], ['right', x, y, 'RIGHT']]
|
||||
if y != 0: possible.append(['move', x, y - 1, 'UP'])
|
||||
return possible
|
||||
case 'RIGHT':
|
||||
possible = [['left', x, y, 'UP'], ['right', x, y, 'DOWN']]
|
||||
if x != (self.cell_number-1): possible.append(['move', x + 1, y, 'RIGHT'])
|
||||
return possible
|
||||
case 'DOWN':
|
||||
possible = [['left', x, y, 'RIGHT'], ['right', x, y, 'LEFT']]
|
||||
if y != (self.cell_number-1): possible.append(['move', x, y + 1, 'DOWN'])
|
||||
return possible
|
||||
case 'LEFT':
|
||||
possible = [['left', x, y, 'DOWN'], ['right', x, y, 'UP']]
|
||||
if x != 0: possible.append(['move', x - 1, y, 'LEFT'])
|
||||
return possible
|
||||
|
||||
def cost(self, node, stones, goal, flowers):
|
||||
# cost = node.distance
|
||||
cost = 0
|
||||
# cost += 10 if stones[node.state[0], node.state[1]] == 1 else 1
|
||||
cost += 1000 if (node.state[0], node.state[1]) in stones else 1
|
||||
cost += 10 if ((node.state[0]), (node.state[1])) in flowers else 1
|
||||
|
||||
if node.parent:
|
||||
node = node.parent
|
||||
cost += node.distance # should return only elem.action in prod
|
||||
return cost
|
||||
|
||||
def heuristic(self, node, goal):
|
||||
return abs(node.state[0] - goal[0]) + abs(node.state[1] - goal[1])
|
||||
|
||||
#bandaid to know about stones
|
||||
def astarsearch(self, istate, goaltest, stone_list, plant_list):
|
||||
|
||||
#to be expanded
|
||||
def cost_old(x, y):
|
||||
if (x, y) in stones:
|
||||
return 10
|
||||
else:
|
||||
return 1
|
||||
|
||||
x = istate[0]
|
||||
y = istate[1]
|
||||
angle = istate[2]
|
||||
stones = []
|
||||
flowers = []
|
||||
|
||||
for obj in stone_list:
|
||||
stones.append((obj.xy[0]*50, obj.xy[1]*50))
|
||||
for obj in plant_list:
|
||||
if obj.name == 'flower':
|
||||
flowers.append((obj.xy[0]*50, obj.xy[1]*50))
|
||||
|
||||
# stones = [(x*50, y*50) for (x, y) in stone_list]
|
||||
# flowers = [(x*50, y*50) for (x, y) in plant_list]
|
||||
|
||||
print(stones)
|
||||
|
||||
# fringe = [(Node([x, y, angle]), cost_old(x, y))] # queue (moves/states to check)
|
||||
fringe = [(Node([x, y, angle]))] # queue (moves/states to check)
|
||||
fringe[0].distance = self.cost(fringe[0], stones, goaltest, flowers)
|
||||
fringe.append((Node([x, y, angle]), self.cost(fringe[0], stones, goaltest, flowers)))
|
||||
fringe.pop(0)
|
||||
|
||||
explored = []
|
||||
|
||||
while True:
|
||||
if len(fringe) == 0:
|
||||
return False
|
||||
|
||||
fringe.sort(key=lambda x: x[1])
|
||||
elem = fringe.pop(0)[0]
|
||||
|
||||
# if goal_test(elem.state):
|
||||
# return
|
||||
# print(elem.state[0], elem.state[1], elem.state[2])
|
||||
if elem.state[0] == goaltest[0] and elem.state[1] == goaltest[1]: # checks if we reached the given point
|
||||
steps = []
|
||||
while elem.parent:
|
||||
steps.append([elem.action, elem.state[0], elem.state[1]]) # should return only elem.action in prod
|
||||
elem = elem.parent
|
||||
|
||||
steps.reverse()
|
||||
print(steps) # only for dev
|
||||
return steps
|
||||
|
||||
explored.append(elem.state)
|
||||
|
||||
for (action, state_x, state_y, state_angle) in self.succ(elem.state):
|
||||
x = Node([state_x, state_y, state_angle], elem, action)
|
||||
x.parent = elem
|
||||
|
||||
priority = self.cost(elem, stones, goaltest, flowers) + self.heuristic(elem, goaltest)
|
||||
elem.distance = priority
|
||||
# priority = cost_old(x, y) + self.heuristic(elem, goaltest)
|
||||
fringe_states = [node.state for (node, p) in fringe]
|
||||
|
||||
if x.state not in fringe_states and x.state not in explored:
|
||||
fringe.append((x, priority))
|
||||
elif x.state in fringe_states:
|
||||
for i in range(len(fringe)):
|
||||
if fringe[i][0].state == x.state:
|
||||
if fringe[i][1] > priority:
|
||||
fringe[i] = (x, priority)
|
||||
|
||||
|
||||
def closest_point(self, x, y, name, plant_list):
|
||||
self.max_distance = self.cell_number*self.cell_number
|
||||
for obj in plant_list:
|
||||
if obj.name == name:
|
||||
if obj.state == 0:
|
||||
self.distance = (abs(obj.xy[0] - x) + abs(obj.xy[1] - y))
|
||||
if self.distance <= self.max_distance:
|
||||
self.max_distance = self.distance
|
||||
x_close = obj.xy[0]
|
||||
y_close = obj.xy[1]
|
||||
#print("distance: ",self.distance, obj.xy[0], "+", obj.xy[1], "-" ,x, "+",y)
|
||||
return (x_close, y_close)
|
||||
@ -1,9 +1,9 @@
|
||||
class Node:
|
||||
def __init__(self, state, parent='', action='', distance=0):
|
||||
def __init__(self, state, parent='', action=''):
|
||||
self.state = state
|
||||
self.parent = parent
|
||||
self.action = action
|
||||
self.distance = distance
|
||||
|
||||
|
||||
class Search:
|
||||
def __init__(self, cell_size, cell_number):
|
||||
@ -17,77 +17,36 @@ class Search:
|
||||
match(angle):
|
||||
case 'UP':
|
||||
possible = [['left', x, y, 'LEFT'], ['right', x, y, 'RIGHT']]
|
||||
if y != 0: possible.append(['move', x, y - 1, 'UP'])
|
||||
if y != 0: possible.append(['move', x, y - self.cell_size, 'UP'])
|
||||
return possible
|
||||
case 'RIGHT':
|
||||
possible = [['left', x, y, 'UP'], ['right', x, y, 'DOWN']]
|
||||
if x != (self.cell_number-1): possible.append(['move', x + 1, y, 'RIGHT'])
|
||||
if x != self.cell_size*(self.cell_number-1): possible.append(['move', x + self.cell_size, y, 'RIGHT'])
|
||||
return possible
|
||||
case 'DOWN':
|
||||
possible = [['left', x, y, 'RIGHT'], ['right', x, y, 'LEFT']]
|
||||
if y != (self.cell_number-1): possible.append(['move', x, y + 1, 'DOWN'])
|
||||
if y != self.cell_size*(self.cell_number-1): possible.append(['move', x, y + self.cell_size, 'DOWN'])
|
||||
return possible
|
||||
case 'LEFT':
|
||||
possible = [['left', x, y, 'DOWN'], ['right', x, y, 'UP']]
|
||||
if x != 0: possible.append(['move', x - 1, y, 'LEFT'])
|
||||
if x != 0: possible.append(['move', x - self.cell_size, y, 'LEFT'])
|
||||
return possible
|
||||
|
||||
def cost(self, node, stones, goal, flowers):
|
||||
# cost = node.distance
|
||||
cost = 0
|
||||
# cost += 10 if stones[node.state[0], node.state[1]] == 1 else 1
|
||||
cost += 1000 if (node.state[0], node.state[1]) in stones else 1
|
||||
cost += 10 if ((node.state[0]), (node.state[1])) in flowers else 1
|
||||
|
||||
if node.parent:
|
||||
node = node.parent
|
||||
cost += node.distance # should return only elem.action in prod
|
||||
return cost
|
||||
|
||||
def heuristic(self, node, goal):
|
||||
return abs(node.state[0] - goal[0]) + abs(node.state[1] - goal[1])
|
||||
|
||||
#bandaid to know about stones
|
||||
def astarsearch(self, istate, goaltest, stone_list, plant_list):
|
||||
|
||||
#to be expanded
|
||||
def cost_old(x, y):
|
||||
if (x, y) in stones:
|
||||
return 10
|
||||
else:
|
||||
return 1
|
||||
|
||||
def graphsearch(self, istate, goaltest):
|
||||
x = istate[0]
|
||||
y = istate[1]
|
||||
angle = istate[2]
|
||||
stones = []
|
||||
flowers = []
|
||||
|
||||
for obj in stone_list:
|
||||
stones.append((obj.xy[0]*50, obj.xy[1]*50))
|
||||
for obj in plant_list:
|
||||
if obj.name == 'flower':
|
||||
flowers.append((obj.xy[0]*50, obj.xy[1]*50))
|
||||
|
||||
# stones = [(x*50, y*50) for (x, y) in stone_list]
|
||||
# flowers = [(x*50, y*50) for (x, y) in plant_list]
|
||||
|
||||
print(stones)
|
||||
|
||||
# fringe = [(Node([x, y, angle]), cost_old(x, y))] # queue (moves/states to check)
|
||||
fringe = [(Node([x, y, angle]))] # queue (moves/states to check)
|
||||
fringe[0].distance = self.cost(fringe[0], stones, goaltest, flowers)
|
||||
fringe.append((Node([x, y, angle]), self.cost(fringe[0], stones, goaltest, flowers)))
|
||||
fringe.pop(0)
|
||||
|
||||
fringe = [Node([x, y, angle])] # queue (moves/states to check)
|
||||
fringe_state = [fringe[0].state]
|
||||
explored = []
|
||||
|
||||
while True:
|
||||
if len(fringe) == 0:
|
||||
return False
|
||||
|
||||
fringe.sort(key=lambda x: x[1])
|
||||
elem = fringe.pop(0)[0]
|
||||
elem = fringe.pop(0)
|
||||
fringe_state.pop(0)
|
||||
|
||||
# if goal_test(elem.state):
|
||||
# return
|
||||
@ -105,32 +64,10 @@ class Search:
|
||||
explored.append(elem.state)
|
||||
|
||||
for (action, state_x, state_y, state_angle) in self.succ(elem.state):
|
||||
x = Node([state_x, state_y, state_angle], elem, action)
|
||||
x.parent = elem
|
||||
|
||||
priority = self.cost(elem, stones, goaltest, flowers) + self.heuristic(elem, goaltest)
|
||||
elem.distance = priority
|
||||
# priority = cost_old(x, y) + self.heuristic(elem, goaltest)
|
||||
fringe_states = [node.state for (node, p) in fringe]
|
||||
|
||||
if x.state not in fringe_states and x.state not in explored:
|
||||
fringe.append((x, priority))
|
||||
elif x.state in fringe_states:
|
||||
for i in range(len(fringe)):
|
||||
if fringe[i][0].state == x.state:
|
||||
if fringe[i][1] > priority:
|
||||
fringe[i] = (x, priority)
|
||||
|
||||
|
||||
def closest_point(self, x, y, name, plant_list):
|
||||
self.max_distance = self.cell_number*self.cell_number
|
||||
for obj in plant_list:
|
||||
if obj.name == name:
|
||||
if obj.state == 0:
|
||||
self.distance = (abs(obj.xy[0] - x) + abs(obj.xy[1] - y))
|
||||
if self.distance <= self.max_distance:
|
||||
self.max_distance = self.distance
|
||||
x_close = obj.xy[0]
|
||||
y_close = obj.xy[1]
|
||||
#print("distance: ",self.distance, obj.xy[0], "+", obj.xy[1], "-" ,x, "+",y)
|
||||
return (x_close, y_close)
|
||||
if [state_x, state_y, state_angle] not in fringe_state and \
|
||||
[state_x, state_y, state_angle] not in explored:
|
||||
x = Node([state_x, state_y, state_angle])
|
||||
x.parent = elem
|
||||
x.action = action
|
||||
fringe.append(x)
|
||||
fringe_state.append(x.state)
|
||||
9
main.py
9
main.py
@ -6,6 +6,8 @@ from core.chicken import chicken as chick
|
||||
from core.field import field_settings
|
||||
from core.plants import plants_settings
|
||||
from agent.methods.genetic_algorithm import genetic_algorithm
|
||||
from agent.methods import a_star
|
||||
|
||||
import numpy as np
|
||||
|
||||
from agent.neural_network import inference
|
||||
@ -13,7 +15,6 @@ from agent.neural_network import inference
|
||||
#import neural_network.inference
|
||||
# import core.plants.plant as plant
|
||||
# import core.plants.plants_settings as plants_settings
|
||||
import agent.methods.graph_search as graph_search
|
||||
|
||||
|
||||
#import models.field_block as field_block
|
||||
@ -59,7 +60,7 @@ class Game:
|
||||
#vegies_list
|
||||
self.Plants.locate_veggies(self.veggies_list, 'pepper', self.blocks_number-5)
|
||||
self.Plants.locate_veggies(self.veggies_list, 'carrot', self.blocks_number-5)
|
||||
self.Plants.locate_veggies(self.veggies_list, 'pumpkin', self.blocks_number-5)
|
||||
self.Plants.locate_veggies(self.veggies_list, 'papaya', self.blocks_number-5)
|
||||
self.Plants.locate_veggies(self.veggies_list, 'wheat', self.blocks_number)
|
||||
|
||||
|
||||
@ -75,8 +76,8 @@ class Game:
|
||||
running = True
|
||||
clock = pygame.time.Clock()
|
||||
move_chicken_event = pygame.USEREVENT + 1
|
||||
pygame.time.set_timer(move_chicken_event, 1000) # chicken moves every 1000 ms
|
||||
self.search_object = graph_search.Search(self.cell_size, self.cell_number)
|
||||
pygame.time.set_timer(move_chicken_event, 500) # chicken moves every 1000 ms
|
||||
self.search_object = a_star.Search(self.cell_size, self.cell_number)
|
||||
chicken_next_moves = []
|
||||
|
||||
veggies = dict()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user