def uniformCostSearch(problem):
"Search the node of least total cost first. "
"*** YOUR CODE HERE ***"
from util import PriorityQueue
frontier = PriorityQueue()
explored_states = set([])
node = (problem.getStartState(), [], 0)
if problem.isGoalState(node[0]):
return node[1]
PriorityQueue.push(frontier, node, node[2])
while True:
if not frontier:
return None
node = PriorityQueue.pop(frontier)
explored_states.add(node[0])
successor_candidates = problem.getSuccessors(node[0])
successor_candidates.reverse()
for candidate in successor_candidates:
if candidate[0] not in explored_states:
path_to_node = list(node[1])
path_to_node.append(candidate[1])
child_node = (candidate[0], path_to_node, node[2] + candidate[2])
# if candidate[0] not in explored_states:
if problem.isGoalState(child_node[0]):
return child_node[1]
PriorityQueue.push(frontier, child_node, child_node[2])
def __guess_and_check(self):
"""If it proves impossible to solve the puzzle by pure deduction, this
method is called to guess (using minimum remaining values and least constraining
choice heuristics) a number, which is then used to solve a reduced game (if
possible). All possible choices are exhausted in this manner.
"""
queue = PriorityQueue()
impossible_list = []
for x, y in product(range(9), repeat=2):
position = self.__positions[x][y]
n = position.num_possible()
if n <= 1:
continue
for val in position.possible:
new_board = deepcopy(self)
new_board.__set_position_val(x, y, val)
new_board.__enforce_consistency()
priority = new_board.__possible_count()
queue.push((new_board, x, y, val), priority*n)
while not queue.isEmpty():
new_board, x, y, val = queue.pop()
for xx, yy, valval in impossible_list:
new_board.__positions[xx][yy].remove_possible(valval)
b = new_board.__solver()
if not isinstance(b, SudokuGame):
impossible_list.append((x, y, val))
continue
return b
return False
class ApproximateSearchAgent(Agent):
"Implement your contest entry here. Change anything but the class name."
def registerInitialState(self, state):
"This method is called before any moves are made."
"*** YOUR CODE HERE ***"
# ApproximateSearchAgent.create(state,set(state.foodGrid.asList()),state.walls)
def getAction(self, state):
"""
From game.py:
The Agent will receive a GameState and must return an action from
Directions.{North, South, East, West, Stop}
"""
"*** YOUR CODE HERE ***"
if 'actionIndex' not in dir(self): self.actionIndex = 0
i = self.actionIndex
self.actionIndex += 1
if i < len(self.actions):
return self.actions[i]
else:
return Directions.STOP
# util.raiseNotDefined()
def __init__(self):
from util import PriorityQueue
self.pacman = tuple()
self.food = []
self.walls = list()
self.Queue = PriorityQueue()
self.min = float("inf")
def create(self,p,f,w):
self.pacman = p
self.food = f
self.walls = w
self.myWalls = w.asList()
def getWalls(self):
return self.walls
def getPacman(self):
return self.pacman
def getFood(self):
return self.food
def getPacmanPosition(self):
return self.pacman
def getNumFood(self):
return len(self.food)
def hasFood(self,*goal):
return len(list(*goal))
def mazeDistance(self,point1,point2):
for food in self.getFood():
if food not in self.myWalls:
prob = PositionSearchProblem(self, start=point1, goal=point2, warn=False)
dist = len(search.bfs(prob))
self.Queue.push(food,dist)
self.min = self.min if self.min<dist else dist
def newQueue(self):
from util import PriorityQueue
self.Queue = PriorityQueue()
def uniformCostSearch(problem):
successor_idx = {'dir': 1, 'state': 0, 'cost': -1}
MAX_ITER = int(2000)
stateQ = PriorityQueue()
visitedSet = set()
successorDict = {}
curState = problem.getStartState()
actionList = [] # add actions to get to the state, use pop to remove last one
currentCost = 0
for it in range(MAX_ITER):
if problem.isGoalState(curState):
return actionList
if curState not in visitedSet:
successorDict[curState] = problem.getSuccessors(curState)
visitedSet.add(curState)
for node in successorDict[curState]:
if node[successor_idx['state']] not in visitedSet:
tmp_state = {'cost' : currentCost + node[successor_idx['cost']], 'action' : actionList + [node[successor_idx['dir']]], 'state': node[successor_idx['state']]}
stateQ.push(tmp_state, tmp_state['cost'])
# get next state to test
nState = stateQ.pop()
actionList = nState['action']
curState = nState['state']
currentCost = nState['cost']
return []
def aStarSearch(problem, heuristic=nullHeuristic):
from collections import defaultdict
from util import PriorityQueue
initial_node=problem.getStartState()
current_node=defaultdict(list)
current_node[initial_node].append("No parent")
current_node[initial_node].append("No direction")
priority_queue=PriorityQueue()
cost_array={}
#priority_queue.push(current_node,0)
cost_list={}
cost_list[initial_node]=0
cost_array[initial_node]=heuristic(initial_node,problem)
direction_list={}
Child=current_node.keys()
Parent=current_node.values()[0]
visited_list={}
while (problem.isGoalState(Child[0]) is not True):
if(Child[0] in visited_list.keys()):
current_node=priority_queue.pop()
Child=current_node.keys()
Parent=current_node.values()[0]
else:
visited_list[Child[0]]=Parent[0]
direction_list[Child[0]]=Parent[1]
Successor_node=problem.getSuccessors(Child[0])
for index in range(len(Successor_node)):
temp_dict=defaultdict(list)
temp_dict[Successor_node[index][0]].append(Child[0])
temp_dict[Successor_node[index][0]].append(Successor_node[index][1])
cost_list[Successor_node[index][0]]= cost_list[Child[0]]+Successor_node[index][2]
cost_array[Successor_node[index][0]]=cost_list[Successor_node[index][0]]+heuristic(Successor_node[index][0],problem)
priority_queue.push(temp_dict, cost_array[Successor_node[index][0]])
current_node=priority_queue.pop()
Child=current_node.keys()
Parent=current_node.values()[0]
visited_list[Child[0]]= Parent[0]
direction_list[Child[0]]=Parent[1]
backtracking =[]
path = Child[0]
backtracking.append(direction_list[path])
path = visited_list[path]
while (path!= problem.getStartState()):
backtracking.append(direction_list[path])
path = visited_list[path]
backtracking.reverse()
return backtracking
util.raiseNotDefined()
def __init__(self, position, blocks, game_state, elements):
self.elements = elements
self.ds = PriorityQueue()
self.position = round_tuple(position)
self.blocks = [round_tuple(t) for t in blocks]
self.game_state = game_state
self.antecessor = {}
def __init__(self):
from util import PriorityQueue
self.pacman = tuple()
self.food = []
self.walls = list()
self.Queue = PriorityQueue()
self.min = float("inf")
def uniformCostSearch(problem):
"Search the node of least total cost first. "
start = problem.getStartState()
frontier = PriorityQueue() # A priority queue of (state,route_to_state,cost)
frontier_set = set() #A set recording what's in the frontier
explored_set = set() # A set of states recording the explored nodes
frontier.push((start,list(),0),0)
frontier_set.add(start)
solution_node = ((0,0),list(),-1)
while not frontier.isEmpty():
current_node = frontier.pop()
frontier_set.remove(current_node[0])
if solution_node[2] == -1 or current_node[2] < solution_node[2]:
if problem.isGoalState(current_node[0]):
solution_node = current_node
successors = list()
else:
successors = problem.getSuccessors(current_node[0])
explored_set.add(current_node[0])
for s in successors:
if s[0] not in explored_set.union(frontier_set):
current_route = list(current_node[1])
current_route.append(s[1])
frontier.push((s[0],current_route,current_node[2]+s[2]),current_node[2]+s[2])
frontier_set.add(s[0])
elif s[0] in frontier_set:
#retrieve the frontier to see if current_node has lower cost
found = False
nodes = list()
while not found:
node = frontier.pop()
if s[0] == node[0]:
found = True
if current_node[2]+s[2] < node[2]:
current_route = list(current_node[1])
current_route.append(s[1])
nodes.append((s[0],current_route,current_node[2]+s[2]))
else:
nodes.append(node)
else:
nodes.append(node)
for n in nodes:
frontier.push(n,n[2])
if solution_node[2] == -1:
print "No route found!"
return solution_node[1]
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
if problem.isGoalState(problem.getStartState()):
return list()
frontier = PriorityQueue()
##same as q1,q2 but keep a sum of stepcosts from starting point to current state
listOfActions = list()
frontier.push( [problem.getStartState(),listOfActions,0],0 )
explored = set()
while not(frontier.isEmpty()):
node = frontier.pop()
if problem.isGoalState(node[0]):
return node[1]
##check if the state has been explored before checking its successors
##avoid expanding unnecessary nodes and perserve two nodes with same states on the frontier
if node[0] not in explored:
for state,action,stepCost in problem.getSuccessors(node[0]):
oldActions = list(node[1])
oldActions.append(action)
##increment total costs by adding this step's cost to total cost
oldCosts = node[2]
oldCosts += stepCost
nextNode = [state,oldActions,oldCosts]
if(state not in explored):
frontier.push(nextNode,oldCosts)
explored.add(node[0])
return None
def aStarSearch(problem, heuristic=nullHeuristic):
"Search the node that has the lowest combined cost and heuristic first."
"*** YOUR CODE HERE ***"
#print 'A* is called'
queue = PriorityQueue()
explored = []
queue.push((problem.getStartState(), [], 0), 0) #what in the queue is the (state, path,cost)
explored.append(problem.getStartState())
while not queue.isEmpty():
tuple = queue.pop()
currentPath = tuple[1]
currentCost = tuple[2]
#explored.append(tuple[0])
if problem.isGoalState(tuple[0]):
return currentPath
suc = problem.getSuccessors(tuple[0])
for triple in suc:
if explored.__contains__(triple[0]):
continue
if not problem.isGoalState(triple[0]):
explored.append(triple[0])
path = currentPath + [triple[1]]
cost = currentCost + triple[2]
fn = cost + heuristic(triple[0], problem)
queue.push((triple[0], path, cost), fn)
def uniformCostSearch(problem):
"Search the node of least total cost first. "
"*** YOUR CODE HERE ***"
queue = PriorityQueue()
explored = []
queue.push((problem.getStartState(), [], 0), 0) #what in the queue is the (state, path,cost)
explored.append(problem.getStartState())
while not queue.isEmpty():
tuple = queue.pop()
#print tuple[0], tuple[2]
currentPath = tuple[1]
#print currentPath
currentCost = tuple[2]
if problem.isGoalState(tuple[0]):
return currentPath
suc = problem.getSuccessors(tuple[0])
for triple in suc:
if explored.__contains__(triple[0]):
continue
if not problem.isGoalState(triple[0]):
explored.append(triple[0])
path = currentPath + [triple[1]]
cost = currentCost + triple[2]
queue.push((triple[0], path, cost), cost)
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
from game import Directions
from util import PriorityQueue
pqueue = PriorityQueue()
startNode = problem.getStartState()
pqueue.push((startNode, []), 0)
visitedList = []
oldCost = {}
oldCost[startNode[0]] = None
while not pqueue.isEmpty():
current, actions = pqueue.pop()
if problem.isGoalState(current):
return actions
visitedList.append(current)
for coord, direction, steps in problem.getSuccessors(current):
new_actions = actions + [direction]
priority = problem.getCostOfActions(new_actions) + heuristic(coord, problem)
if not coord in visitedList or priority < oldCost[coord[0]]:
oldCost[coord[0]] = priority
visitedList.append(coord)
pqueue.push((coord, new_actions), priority)
return []
util.raiseNotDefined()
def uniformCostSearch(problem):
"Search the node of least total cost first. "
closed = []
closedSet = sets.Set(closed)
fringe = PriorityQueue()
for child in problem.getSuccessors(problem.getStartState()):
path = [child[1]]
fringe.push((child[0], path, child[2]), child[2])
closedSet.add(problem.getStartState())
while( not(fringe.isEmpty()) ):
nodePos, nodeDir, nodeCost = fringe.pop()
if (problem.isGoalState(nodePos)):
return nodeDir
if (nodePos not in closedSet):
successors = problem.getSuccessors(nodePos)
closedSet.add(nodePos)
path = [nodeDir]
for childNode in successors:
cost = nodeCost + childNode[2]
path = nodeDir + [childNode[1]]
childNode = [childNode[0], path , cost]
fringe.push(childNode, cost)
return -1
def uniformCostSearch(problem):
"Search the node of least total cost first. "
"*** YOUR CODE HERE ***"
from util import PriorityQueue
# fringe
fringe = PriorityQueue()
# closed set
closed = set([])
fringe.push((problem.getStartState(), [], 0), 0)
while True:
if fringe.isEmpty() == True:
print 'fail to find a path to the goal state'
return []
else:
node = fringe.pop()
if problem.isGoalState(node[0]) == True:
return node[1]
if node[0] not in closed:
closed.add(node[0])
actionsSoFar = node[1]
costSoFar = node[2]
for successor in problem.getSuccessors(node[0]):
newActions = actionsSoFar[:]
newActions.append(successor[1])
newCost = costSoFar
newCost += successor[2]
fringe.push((successor[0], newActions, newCost), newCost)
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
# Visited
visited = []
# Declare priority queue
priorityQueueNode = PriorityQueue()
# Start state
priorityQueueNode.push((problem.getStartState(),[]),0)
while not priorityQueueNode.isEmpty():
currentState, actions = priorityQueueNode.pop()
if currentState not in visited:
#print "Current node ", currentNode
if problem.isGoalState(currentState):
return actions
visited.append(currentState)
seccessors = problem.getSuccessors(currentState)
for state, action, cost in seccessors:
if state not in visited:
tempActions = actions + [action]
priorityQueueNode.push((state, tempActions), problem.getCostOfActions(tempActions))
#visited.append(state)
return []
def uniformCostSearch(problem):
"Search the node of least total cost first. "
"*** YOUR CODE HERE ***"
from util import PriorityQueue
pathConstructDict = {}
priorityQueue = PriorityQueue()
costToNode = {}
for successor in problem.getSuccessors(problem.getStartState()):
priorityQueue.push((successor, None), successor[2]) #push successor and its priority is its path cost
costToNode[problem.getStartState()] = 0
while(not priorityQueue.isEmpty()):
expansion = priorityQueue.pop()
pathConstructDict[expansion[0]] = expansion[1]
if(expansion[1] != None):
costToNode[expansion[0][0]] = costToNode[expansion[1][0]] + expansion[0][2]
else:
costToNode[expansion[0][0]] = expansion[0][2]
if(problem.isGoalState(expansion[0][0])):
return pathConstructor(expansion[0], pathConstructDict)
for successor in problem.getSuccessors(expansion[0][0]):
if(successor[0] not in costToNode):
priorityQueue.push((successor, expansion[0]), costToNode[expansion[0][0]] + successor[2])
return None
def aStarSearch(problem, heuristic=nullHeuristic):
"Search the node that has the lowest combined cost and heuristic first."
"*** YOUR CODE HERE ***"
#A* is just UCS with g(n) + h(n)
from util import PriorityQueue
pathConstructDict = {}
priorityQueue = PriorityQueue()
costToNode = {}
for successor in problem.getSuccessors(problem.getStartState()):
priorityQueue.push((successor, None), successor[2] + heuristic(successor[0], problem)) #push successor and its priority is its path cost for a* it is g(n) + h(n)
costToNode[problem.getStartState()] = 0
while(not priorityQueue.isEmpty()):
expansion = priorityQueue.pop()
pathConstructDict[expansion[0]] = expansion[1]
if(expansion[1] != None):
costToNode[expansion[0][0]] = costToNode[expansion[1][0]] + expansion[0][2]
else:
costToNode[expansion[0][0]] = expansion[0][2]
if(problem.isGoalState(expansion[0][0])):
return pathConstructor(expansion[0], pathConstructDict)
for successor in problem.getSuccessors(expansion[0][0]):
if(successor[0] not in costToNode):
priorityQueue.push((successor, expansion[0]), costToNode[expansion[0][0]] + successor[2] + heuristic(successor[0], problem))
return None
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
# Visited
visited = []
# Declare priority queue
priorityQueueNode = PriorityQueue()
# Start state
priorityQueueNode.push((problem.getStartState(),[]),0)
while not priorityQueueNode.isEmpty():
currentState, actions = priorityQueueNode.pop()
if currentState not in visited:
#print "Current node ", currentNode
if problem.isGoalState(currentState):
return actions
visited.append(currentState)
seccessors = problem.getSuccessors(currentState)
for state, action, cost in seccessors:
if state not in visited:
tempActions = actions + [action]
priorityQueueNode.push((state, tempActions), problem.getCostOfActions(tempActions) + heuristic(state, problem))
return []
def aStarSearch(problem, heuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
"""
Only A* can find dots in openMaze. Path looks like a staircase.
"""
path = list()
parentChild = list()
print "Problem: ", problem
print "Start:", problem.getStartState()
print "Is the start a goal?", problem.isGoalState(problem.getStartState())
print "Start's successors:", problem.getSuccessors(problem.getStartState())
if problem.isGoalState(problem.getStartState()):
return None
explored = list()
frontier = PriorityQueue()
#tuple = (problem.getStartState(), float("inf"))
frontier.push(problem.getStartState(), float("inf"))
while(not frontier.isEmpty()):
state = frontier.pop() #state = [(x, y), cost]
explored.append(state)
if (problem.isGoalState(state)):
#print "Found..."
path = backtracking(problem, state, parentChild)
return path
for successor in problem.getSuccessors(state):
#print "Successor: ", successor
if (not successor[0] in explored):
#print("State[1] ", state[1])
cost = successor[2] + heuristic(state, problem) #cost = stepCost +currentNodeCost
parentChild.append((state, successor[1], successor[0]))
frontier.push(successor[0], cost)
return None
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
path = list()
parentChild = list()
print "Problem: ", problem
print "Start:", problem.getStartState()
print "Is the start a goal?", problem.isGoalState(problem.getStartState())
print "Start's successors:", problem.getSuccessors(problem.getStartState())
if problem.isGoalState(problem.getStartState()):
return None
explored = list()
frontier = PriorityQueue()
#tuple = (problem.getStartState(), float("inf"))
frontier.push(problem.getStartState(), float("inf"))
while(not frontier.isEmpty()):
state = frontier.pop() #state = [(x, y), cost]
explored.append(state)
if (problem.isGoalState(state)):
#print "Found..."
path = backtracking(problem, state, parentChild)
return path
for successor in problem.getSuccessors(state):
#print "Successor: ", successor
if (not successor[0] in explored):
cost = successor[2] + state[1]
parentChild.append((state, successor[1], successor[0]))
frontier.push(successor[0], cost)
return None
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
from game import Directions
from util import PriorityQueue
s = Directions.SOUTH
w = Directions.WEST
e = Directions.EAST
n = Directions.NORTH
visited = [None] # list of states (each state is a position in this case)
pq = PriorityQueue() # contains pairs. each pair's first elem is a list of actions. second elem is state
pq.push([[], problem.getStartState()], 0)
while (not pq.isEmpty()):
top = pq.pop() # this is the top of the stack
actions = top[0]
state = top[1]
if (state not in visited):
if problem.isGoalState(state):
return actions
visited.append(state)
for child in problem.getSuccessors(state):
tempActions = actions[:]
tempActions.append(child[1])
totalCostOfActions = problem.getCostOfActions(tempActions) + heuristic(child[0], problem)
pq.push([tempActions, child[0]], totalCostOfActions)
return None
def aStarSearch(problem, heuristic=nullHeuristic):
"Search the node that has the lowest combined cost and heuristic first."
closed = []
closedSet = sets.Set(closed)
fringe = PriorityQueue()
for child in problem.getSuccessors(problem.getStartState()):
path = [child[1]]
fringe.push((child[0], path, child[2]), child[2] + heuristic(child[0], problem))
closedSet.add(problem.getStartState())
while( not(fringe.isEmpty()) ):
nodePos, nodeDir, nodeCost = fringe.pop()
if (problem.isGoalState(nodePos)):
return nodeDir
if (nodePos not in closedSet):
successors = problem.getSuccessors(nodePos)
closedSet.add(nodePos)
path = [nodeDir]
for childNode in successors:
cost = nodeCost + childNode[2]
path = nodeDir + [childNode[1]]
childNode = [childNode[0], path , cost]
fringe.push(childNode, cost + heuristic(childNode[0], problem))
return -1
def aStarSearch(problem, heuristic=nullHeuristic):
closedSet = set()
openSet = PriorityQueue()
visited = set()
start = problem.getStartState()
cameFrom = dict()
gScore = dict()
gScore[start] = 0
fScore = dict()
fScore[start] = heuristic(start, problem)
openSet.push(start, fScore[start])
visited.add(start)
while not openSet.isEmpty():
current = openSet.pop()
if problem.isGoalState(current):
return reconstruct(cameFrom, current, start)
closedSet.add(current)
for successor in problem.getSuccessors(current):
succ = successor[0]
action = successor[1]
cost = successor[2]
#(successor, action, stepCost)
if succ in closedSet:
continue
fromStart = gScore[current] + cost
if succ in visited and fromStart >= gScore[succ]:
continue
cameFrom[succ] = (current, action)
gScore[succ] = fromStart
fScore[succ] = fromStart + heuristic(succ, problem)
visited.add(succ)
openSet.push(succ, fScore[succ])
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
fringe = PriorityQueue()
closed = []
startState = (problem.getStartState(), None, 0, [], 0)
fringe.push(startState, 0)
while True:
if fringe.isEmpty():
util.raiseNotDefined()
node = fringe.pop()
if problem.isGoalState(node[0]):
return node[3]
if node[0] not in closed:
closed.append(node[0])
for child in problem.getSuccessors(node[0]):
childNode = list(child)
childNode.append(list(node[3]))
childNode[3].append(childNode[1])
childNode.append(childNode[2] + node[4])
fringe.push(childNode, childNode[4] + heuristic(childNode[0], problem))
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
fringe = PriorityQueue()
closed = []
startState = (problem.getStartState(), None, 0, [])
fringe.push(startState, 0)
while True:
if fringe.isEmpty():
return None #failure?
node = fringe.pop()
if problem.isGoalState(node[0]):
return node[3]
if node[0] not in closed:
closed.append(node[0])
for child in problem.getSuccessors(node[0]):
childNode = list(child)
childNode.append(list(node[3]))
childNode[3].append(childNode[1])
fringe.push(childNode, len(childNode[3]))
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
from game import Directions
from util import PriorityQueue
pqueue = PriorityQueue()
startNode = problem.getStartState()
pqueue.push((startNode, []), 0)
visitedList = []
oldCost = {}
oldCost[startNode] = None
while not pqueue.isEmpty():
current, actions = pqueue.pop()
if problem.isGoalState(current):
return actions
visitedList.append(current)
for coord, direction, steps in problem.getSuccessors(current):
new_actions = actions + [direction]
priority = problem.getCostOfActions(new_actions)
if not coord in visitedList or priority < oldCost[coord]:
oldCost[coord] = priority
visitedList.append(coord)
pqueue.push((coord, new_actions), priority)
return []
util.raiseNotDefined()
def aStarSearch(problem, heuristic=nullHeuristic):
from util import PriorityQueue
from game import Directions
actions = []
frontier = PriorityQueue()
frontier.push((problem.getStartState(), [], 0), 0)
visited = []
while (frontier.isEmpty() == False):
(currentS, currentP, currentC) = frontier.pop()
if (problem.isGoalState(currentS) == True):
actions = currentP
break
if (visited.count(currentS) == 0):
visited.append(currentS)
successors = problem.getSuccessors(currentS)
for i in range(0,len(successors)):
(neighbor, direction, cost) = successors[i]
if (visited.count(neighbor) == 0):
frontier.push((neighbor, (currentP +[direction]), (currentC + cost)), (currentC + cost + heuristic(neighbor, problem)))
return actions
util.raiseNotDefined()
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
queueOpen = PriorityQueue()
rootNode = SearchNode(problem.getStartState(), None, None, 0, 0)
if problem.isGoalState(rootNode.position):
return []
queueOpen.push(rootNode, 0)
visited = {}
while not queueOpen.isEmpty():
currentNode = queueOpen.pop()
if currentNode.position in visited:
continue
if problem.isGoalState(currentNode.position):
return currentNode.backtrack()
visited[currentNode.position] = True
for succ in expand(problem, currentNode):
if succ.position not in visited:
temp = SearchNode(succ.position, currentNode, succ.transition, succ.cost, 0)
queueOpen.push(temp, succ.cost)
def aStarSearch(problem, heuristic=nullHeuristic):
"Search the node that has the lowest combined cost and heuristic first."
"*** YOUR CODE HERE ***"
from util import PriorityQueue
frontier = PriorityQueue()
node = (problem.getStartState(), [], 0)
PriorityQueue.push(frontier, node, node[2] + heuristic(node[0], problem))
explored_states = set([])
if problem.isGoalState(node[0]):
return node[1]
elif PriorityQueue.isEmpty(frontier):
return None
else:
while not PriorityQueue.isEmpty(frontier):
new_node = PriorityQueue.pop(frontier)
explored_states.add(new_node[0])
successor_candidates = problem.getSuccessors(new_node[0])
if node[0][0] == (13, 5):
print "successors: ", successor_candidates
if successor_candidates:
successor_candidates.reverse()
for candidate in successor_candidates:
if candidate[0] not in explored_states:
c_path = list(new_node[1])
c_path.append(candidate[1])
# if candidate[0] not in explored_states:
if problem.isGoalState(candidate[0]):
return c_path
PriorityQueue.push(frontier, (candidate[0], c_path, new_node[2] + candidate[2]), new_node[2] + heuristic(candidate[0], problem))
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
fringe = PriorityQueue()
closed = []
startState = (problem.getStartState(), None, 0, [], 0)
fringe.push(startState, 0)
while True:
if fringe.isEmpty():
util.raiseNotDefined()
node = fringe.pop()
if problem.isGoalState(node[0]):
return node[3]
if node[0] not in closed:
closed.append(node[0])
for child in problem.getSuccessors(node[0]):
childNode = list(child)
childNode.append(list(node[3]))
childNode[3].append(childNode[1])
childNode.append(childNode[2] + node[4])
fringe.push(childNode, childNode[4])
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
priorityQueue = PriorityQueue()
priorityQueue.push((problem.getStartState(), [], 0), 0)
visited = []
while not priorityQueue.isEmpty():
temp, path, cost = priorityQueue.pop()
if problem.isGoalState(temp):
return path
if temp not in visited:
visited.append(temp)
for i in problem.getSuccessors(temp):
temp = (i[0], path + [i[1]], cost + i[2])
priorityQueue.update(temp, cost + i[2])
util.raiseNotDefined()
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
visited = set() # close table for graph search
q = PriorityQueue()
#each item of queue is (pos,actions) where actions is a tuple
#and should not change is the feature
q.push((problem.getStartState(), ()), problem.getCostOfActions([]))
while not q.isEmpty():
curState, actions = q.pop()
if problem.isGoalState(curState):
return list(actions)
if curState in visited:
continue
visited.add(curState)
successors = problem.getSuccessors(curState)
for (nextState, action, cost ) in successors:
newActions = list(actions) #make a deep copy
newActions.append(action)
q.push((nextState, tuple(newActions)), problem.getCostOfActions(newActions))
util.raiseNotDefined()
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
if problem.isGoalState(problem.getStartState()):
return []
from util import PriorityQueue
q = PriorityQueue()
visited = []
start = problem.getStartState()
q.push((start, [], 0), 0)
while not q.isEmpty():
node, path, cost_till_here = q.pop()
if problem.isGoalState(node):
return path
elif node not in visited:
visited.append(node)
for successor in problem.getSuccessors(node):
if successor[0] not in visited:
cost = successor[2] + cost_till_here
q.push((successor[0], path + [successor[1]], cost), cost)
return []
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
from util import PriorityQueue
# Cria noh do estado inicial
initial_state = problem.getStartState()
node = Node(state=initial_state,
path=[],
path_cost=0,
obj_cost=heuristic(initial_state, problem))
# Fronteira
openSet = PriorityQueue()
openSet.push(node, node.getCost())
# Nos explorados
closedSet = []
while not openSet.isEmpty():
# Escolhe noh com menor custo
node = openSet.pop()
closedSet.append(node)
# Checa se estado atual eh estado meta
if problem.isGoalState(node.state):
return node.path
# Gera os filhos do estado atual
for state, direction, cost in problem.getSuccessors(node.state):
newNodePath = node.path + [direction]
newNodePathCost = node.path_cost + cost
newNode = Node(state=state,
path=newNodePath,
path_cost=newNodePathCost,
obj_cost=heuristic(state, problem))
if newNode not in closedSet:
# Adiciona novo noh caso ainda nao exista
# se jah existir noh atualiza o custo se o novo custo for menor
openSet.update(newNode, newNode.getCost())
# Caso nao encontre nenhum caminho, nao faz nada
return []
def aStarSearch(problem,
heuristic=nullHeuristic): # heuristic = func(state, problem)
"""Search the node that has the lowest combined cost and heuristic first."""
from util import PriorityQueue
actions = PriorityQueue()
explored = set()
actions.push((problem.getStartState(), []),
heuristic(problem.getStartState(), problem))
while actions.isEmpty() == False:
state, action = actions.pop()
if problem.isGoalState(state):
return action
if state not in explored:
explored.add(state)
for successor, act, cost in problem.getSuccessors(state):
actions.push((successor, action + [act]),
cost + problem.getCostOfActions(action) +
heuristic(successor, problem))
util.raiseNotDefined()
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
frontier = PriorityQueue()
start = problem.getStartState()
path = []
frontier.push((start, path, 0), 0)
explored = []
while not frontier.isEmpty():
cur_state, cur_path, cur_cost = frontier.pop()
if cur_state not in explored:
explored.append(cur_state)
if problem.isGoalState(cur_state):
return cur_path
successors = problem.getSuccessors(cur_state)
for successor, action, cost in successors:
new_path = cur_path + [action]
new_cost = cur_cost + cost
new_heuristic_cost = new_cost + heuristic(successor, problem)
frontier.push((successor, new_path, new_cost),
new_heuristic_cost)
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
pq = PriorityQueue()
startState = problem.getStartState()
visitedList = {}
action = []
cost = 0
pq.push((startState, action, cost), cost)
while not pq.isEmpty():
head = pq.pop()
if problem.isGoalState(head[0]):
return head[1]
if head[0] not in visitedList:
visitedList[head[0]] = True
for successor_nodes, act, co in problem.getSuccessors(head[0]):
if successor_nodes and successor_nodes not in visitedList:
pq.push((successor_nodes, head[1] + [act], head[2] + co),
head[2] + co + heuristic(successor_nodes, problem))
util.raiseNotDefined()
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
priorityQueue = PriorityQueue()
priorityQueue.push((problem.getStartState(), [], 0), 0)
visited = []
while not priorityQueue.isEmpty():
temp, path, cost = priorityQueue.pop()
if problem.isGoalState(temp):
return path
if temp not in visited:
visited.append(temp)
for i in problem.getSuccessors(temp):
temp = (i[0], path + [i[1]], cost + i[2])
priorityQueue.update(temp, cost + i[2] + heuristic(i[0], problem))
util.raiseNotDefined()
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
open_list = PriorityQueue()
open_list.push([(problem.getStartState(), '', 0)], 0)
visited_nodes = []
final_path = []
while not open_list.isEmpty():
node = open_list.pop()
state = node[-1][0]
if problem.isGoalState(state):
for n in node:
if n[1] != '':
final_path.append(n[1])
return final_path
if state not in visited_nodes:
visited_nodes.append(state)
for successor in problem.getSuccessors(state):
new_path = node[:] + [successor]
new_path_actions = []
for n in node:
if n[1] != '':
new_path_actions.append(n[1])
new_path_actions.append(successor[1])
new_path_cost = problem.getCostOfActions(new_path_actions) + heuristic(successor[0], problem)
open_list.push(new_path, new_path_cost)
return []
def wAstarSearch(self, gameState):
myPos = gameState.getAgentState(self.index).getPosition()
actions = []
from util import PriorityQueue
open = PriorityQueue()
closed = {}
closed[gameState] = 999999
open.push((gameState, [], 0), 0 + self.foodHeuristic(gameState))
while not open.isEmpty():
gameState, actions, cost = open.pop()
if gameState not in closed.keys() or closed[gameState] > cost + self.foodHeuristic(gameState):
closed[gameState] = cost + self.foodHeuristic(gameState)
if self.isFoodTerminal(gameState):
return actions
else:
currentActions = gameState.getLegalActions(self.index)
for action in currentActions:
successor = self.getSuccessor(gameState, action)
open.update((successor, actions+[action], cost+1), cost+1+self.foodHeuristic(successor))
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
# python pacman.py -l bigMaze -z .5 -p SearchAgent -a fn=astar,heuristic=manhattanHeuristic --frameTime 0
# python pacman.py -l mediumMaze -p SearchAgent -a fn=astar,heuristic=manhattanHeuristic --frameTime 0
# If start state is the goal, stop and move nowhere
if (problem.isGoalState(problem.getStartState())):
return ['Stop']
from util import PriorityQueue
queue = PriorityQueue()
queue.push((problem.getStartState(), [], 0), 0)
# Created a list of visited nodes
visited = set()
while not queue.isEmpty():
#Pop the first item from the queue
currentPos, currentPath, currentCost = queue.pop()
# print "current pos: ", currentPos, "\t current cost: ", currentCost
# if solution found, exit
if (problem.isGoalState(currentPos)):
return currentPath
# If it hasn't been visited before, perform this:
if (not currentPos in visited):
visited.add(currentPos)
for successor in problem.getSuccessors(
currentPos): # For every successor of the current node
newPath = list(currentPath) # copy the current path
newPath.append(
successor[1]) # add the new direction to the path
newCost = currentCost + successor[
2] # add the new cost (for clarity) + the heuristic value
queue.push(
(successor[0], newPath, newCost), newCost +
heuristic(successor[0], problem)) # push into the queue
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
startState = (problem.getStartState(), "", 0)
paths = PriorityQueue()
paths.push({"path": [startState], "cost": startState[2]}, startState[2])
visitedNodes = []
while not paths.isEmpty():
minPath = paths.pop()
currentPosition = minPath["path"][-1][0]
if not currentPosition in visitedNodes:
visitedNodes.append(currentPosition)
if problem.isGoalState(currentPosition):
return [p[1] for p in minPath["path"][1:]]
successors = problem.getSuccessors(currentPosition)
for successor in successors:
if not successor[0] in visitedNodes:
newPath = {
"path": minPath["path"] + [successor],
"cost": minPath["cost"] + successor[2]
}
paths.push(newPath, newPath["cost"])
def aStarSearch(problem, heuristic=nullHeuristic):
from util import Queue
from util import Stack
from util import PriorityQueue
from game import Directions
#Initialize structures
start = problem.getStartState()
frontier = PriorityQueue()
cost = {}
partial_sols = {}
solution = []
#Using for start node
frontier.push(start, 0)
cost[start] = 0
partial_sols[start] = solution[:]
#Start moving through nodes
while not frontier.isEmpty():
current = frontier.pop()
solution = partial_sols[current]
#Check problem solution
if problem.isGoalState(current):
return partial_sols[current]
#possible movements
for successor in problem.getSuccessors(current):
nextnode, action, action_cost = successor
new_cost = cost[
current] + action_cost #Amount cost + move cost + heuristics
if nextnode not in cost or new_cost < cost[
nextnode]: #node has no cost assigned or newcost is less than the current cost
cost[nextnode] = new_cost #update cost
priority = new_cost + heuristic(nextnode, problem)
frontier.push(nextnode,
priority) #set item with the cost as priority
newsolution = solution[:] #copy current solution and add the action
newsolution.append(action)
partial_sols[nextnode] = newsolution
util.raiseNotDefined()
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
fringe = PriorityQueue()
counts = Counter()
current = (problem.getStartState(), [])
counts[str(current[0])] += heuristic(current[0], problem)
fringe.push(current, counts[str(current[0])])
closed = []
while not fringe.isEmpty():
node, path = fringe.pop()
if problem.isGoalState(node):
return path
if not node in closed:
closed.append(node)
for coord, move, cost in problem.getSuccessors(node):
newpath = path + [move]
counts[str(coord)] = problem.getCostOfActions(newpath)
counts[str(coord)] += heuristic(coord, problem)
fringe.push((coord, newpath), counts[str(coord)])
def foodAStarSearch(start, goal, walls):
from util import PriorityQueue
moveVector = [(1, 0), (0, 1), (-1, 0), (0, -1)]
pq = PriorityQueue()
explored = {}
maxWidth = abs(start[0] - goal[0])
maxHeight = abs(start[1] - goal[1])
if start == goal:
return 0
pq.push((start, 0, 0), 0)
explored[start] = True
nodesPopped = 0
while not pq.isEmpty():
currNode = pq.pop()
currPos, currCost, currSteps = currNode
for move in moveVector:
nextPos = (currPos[0] + move[0], currPos[1] + move[1])
if nextPos not in explored:
# check that move is within bounds
nextWidth = abs(nextPos[0] - goal[0])
nextHeight = abs(nextPos[1] - goal[1])
if nextWidth <= maxWidth and nextHeight <= maxHeight:
if not walls[nextPos[0]][nextPos[1]]:
#check for goal
if nextPos == goal:
return currSteps + 1
explored[nextPos] = True
nextCost = ((nextPos[0] - goal[0]) ** 2 + (nextPos[1] - goal[1]) ** 2) ** 0.5
pq.push((nextPos, nextCost, currSteps + 1), nextCost)
return 999999
def AStar(self, gameState, destination):
from util import PriorityQueue
visited, movements, costs, start_cost = [], {}, {}, 0
startAgentState = gameState.getAgentState(self.index)
startLoc = startAgentState.getPosition()
movements[startLoc] = []
costs[startLoc] = 0
visited.append(startLoc)
priorityQueue = PriorityQueue()
priorityQueue.push(gameState, start_cost)
while not priorityQueue.isEmpty():
currGameState = priorityQueue.pop()
currLoc = currGameState.getAgentState(self.index).getPosition()
if currLoc == destination:
# print "movements: ",movements[currLoc]
return movements[currLoc]
# return movements[currGameState]
actions = currGameState.getLegalActions(self.index)
for action in actions:
succGameState = self.getSuccessor(currGameState, action)
succLoc = succGameState.getAgentState(self.index).getPosition()
newCost = 1
next_cost = costs[currLoc] + newCost
if succLoc not in visited or next_cost < costs[succLoc]:
visited.append(succLoc)
movements[succLoc] = []
pre = movements[currLoc][:]
pre.append(action)
movements[succLoc].extend(pre)
costs[succLoc] = next_cost
heurisitic = self.Heuristic(succLoc, destination)
priority = next_cost + heurisitic
priorityQueue.push(succGameState, priority)
def runValueIteration(self):
"*** YOUR CODE HERE ***"
prob = self.mdp
predecessors = self.getPredecessors(prob)
from util import PriorityQueue
heap = PriorityQueue()
states = prob.getStates()
for state in states:
if not prob.isTerminal(state):
actions = prob.getPossibleActions(state)
value = []
for action in actions:
value.append(self.computeQValueFromValues(state, action))
diff = abs(self.values[state] - max(value))
heap.push(state, -1 * diff)
for i in range(self.iterations):
if heap.isEmpty():
break
s = heap.pop()
if not prob.isTerminal(s):
self.bellmanUpdate(s)
if predecessors[s]:
for pred in predecessors[s]:
predactions = prob.getPossibleActions(pred)
value = []
for predaction in predactions:
value.append(
self.computeQValueFromValues(pred, predaction))
preddiff = abs(self.values[pred] - max(value))
if preddiff > self.theta:
heap.push(pred, -1 * preddiff)
def uniformCostSearch(problem):
'''
return a path to the goal
'''
# TODO 07
from util import PriorityQueue
frontier = PriorityQueue()
frontier.push((problem.getStartState(), [], 0), 0)
print(problem.getStartState())
visited = []
while not frontier.isEmpty():
#print(f"\n\nfrontier: {frontier}")
curNode, path, curCost = frontier.pop()
#print(f"\n\tcurCost: {curCost}, curNode: {curNode}, path: {path}")
if curNode not in visited:
visited.append(curNode)
if problem.isGoalState(curNode):
print(f"Path to goal: {path}")
return path
successors = problem.getSuccessors(curNode)
#print(f"\n\t\t{successors}")
for neighborNode, direction, cost in successors:
#if neighbor not in visited:
print(
f"\n\nneighbor: {neighborNode}, direction: {direction}, cost: {cost}"
)
frontier.update(
(neighborNode, path + [direction], curCost + cost),
curCost + cost)
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
stateQueue = PriorityQueue()
state = problem.getStartState()
state = [(state, "Stop", 0)]
stateQueue.push(state, 0)
while not problem.isGoalState(state[len(state) - 1][0]):
state = stateQueue.pop()
successors = problem.getSuccessors(state[len(state) - 1][0])
for successor in successors:
if successor[0] not in [position[0] for position in state]:
tmp = state + [successor]
stateQueue.push(
tmp,
problem.getCostOfActions([action[1] for action in tmp]))
return [direction[1] for direction in state]
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
state = ()
state = problem.getStartState()
costTillNow = 0
actionsTillnow = []
visited_states = []
new_pq = PriorityQueue()
new_pq.push((state, actionsTillnow, costTillNow), costTillNow)
while True:
temp = new_pq.pop()
state = temp[0]
costTillNow = temp[2]
actionsTillnow = temp[1]
if (problem.isGoalState(state)):
print "Done!!"
return actionsTillnow
break
elif (not (state in visited_states)):
visited_states.append(state)
else:
continue
# successors=()
successors = problem.getSuccessors(state)
# print state
# print costTillNow
# print actionsTillnow
# print successors
# temp_actions=[]
for i in successors:
temp_actions = actionsTillnow[:]
temp_actions.append(i[1])
tempCost = costTillNow + i[2]
new_pq.push((i[0], temp_actions, tempCost), tempCost)
util.raiseNotDefined()
def aStarSearch(problem, heuristic=nullHeuristic):
"Search the node that has the lowest combined cost and heuristic first."
"*** YOUR CODE HERE ***"
start_state = problem.getStartState()
frontier = PriorityQueue() # queue for frontier
explored = set()
frontier.push((start_state, [], 0), heuristic(start_state, problem))
# [] -> path required to reach here
# 0 step cost to reach here
# heuristic(start_state, problem) estimated heurisic distance to reach goal
while not frontier.isEmpty():
location, path_to_reach_here, cost_to_reach_here = frontier.pop()
# explored.add(location)
if problem.isGoalState(location):
return path_to_reach_here
# print location, list(explored)
# if location not in explored:
# print "location not in (explored)"
explored.add(location)
for neighbor in problem.getSuccessors(location):
neighbor_location, neighbor_action, neighbor_step_cost = neighbor
frontier_list = [
element[0] for element in frontier.heap
] # get a list of all elements in frontier s.union(t)
# frontier_union_explored = list(set(frontier_list) + explored)
frontier_union_explored = list(set(frontier_list).union(explored))
if neighbor_location not in frontier_union_explored:
neighbor_total_cost_heuristic = cost_to_reach_here + neighbor_step_cost + heuristic(
neighbor_location, problem) # fn
path_till_neighbor = path_to_reach_here + [neighbor_action]
cost_to_reach_to_neighbor = cost_to_reach_here + neighbor_step_cost
frontier.push((neighbor_location, path_till_neighbor,
cost_to_reach_to_neighbor),
neighbor_total_cost_heuristic)
elif neighbor_location in frontier_list:
print "Take care"
def aStarSearch(problem, heuristic=nullHeuristic):
"Search the node that has the lowest combined cost and heuristic first."
"*** YOUR CODE HERE ***"
#A* is just UCS with g(n) + h(n)
from util import PriorityQueue
pathConstructDict = {}
priorityQueue = PriorityQueue()
costToNode = {}
for successor in problem.getSuccessors(problem.getStartState()):
priorityQueue.push(
(successor, None), successor[2] + heuristic(successor[0], problem)
) #push successor and its priority is its path cost for a* it is g(n) + h(n)
costToNode[problem.getStartState()] = 0
while (not priorityQueue.isEmpty()):
expansion = priorityQueue.pop()
pathConstructDict[expansion[0]] = expansion[1]
if (expansion[1] != None):
costToNode[expansion[0]
[0]] = costToNode[expansion[1][0]] + expansion[0][2]
else:
costToNode[expansion[0][0]] = expansion[0][2]
if (problem.isGoalState(expansion[0][0])):
return pathConstructor(expansion[0], pathConstructDict)
for successor in problem.getSuccessors(expansion[0][0]):
if (successor[0] not in costToNode):
priorityQueue.push((successor, expansion[0]),
costToNode[expansion[0][0]] + successor[2] +
heuristic(successor[0], problem))
return None
def aStarSearch(self, currentPosition, goalStates, nonGoalStates,
gameState):
from util import PriorityQueue
frontier = PriorityQueue()
actions = []
closed = []
evaluation = 0
cost = 0
minimum = 10000
if currentPosition in goalStates:
return None
for positions in self.defensivePositions:
distance = self.getMazeDistance(positions, currentPosition)
if distance < minimum:
minimum = distance
frontier.push((currentPosition, []), minimum)
while (True):
if frontier.isEmpty():
return None
node, path = frontier.pop()
if node not in closed:
closed.append(node)
if node in goalStates:
return path
for successor in self.getSuccessors(node):
actions = path + [successor[1]]
cost += 1
evaluation = cost + self.heuristic(
gameState, successor[0], goalStates, nonGoalStates)
frontier.push((successor[0], actions), evaluation)
util.raiseNotDefined()
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
priorityqueue = PriorityQueue()
priorityqueue.push((problem.getStartState(), [], 0),0)
visitedNode = []
while priorityqueue:
current = priorityqueue.pop()
if current[0] not in visitedNode:
visitedNode.append(current[0])
if problem.isGoalState(current[0]):
return current[1]
nextNodes = problem.getSuccessors(current[0])
for neighbours in nextNodes:
currentPath = current[1] + [neighbours[1]]
currentCost = current[2] + neighbours[2]
priorityqueue.push((neighbours[0],currentPath, currentCost),currentCost)
def aStarSearch(problem, heuristic=nullHeuristic): # it is almost the same code as ucs above but with some minor differences again
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
#util.raiseNotDefined()
from util import PriorityQueue
path_to_node = []
path_cost = 0
problem_start_state = problem.getStartState()
start_node = [problem_start_state, path_to_node] # no need to store the path_cost at the node this time
frontier = PriorityQueue()
frontier.push(start_node, path_cost)
explored = set()
while True:
if frontier.isEmpty():
return []
parent = frontier.pop()
if problem.isGoalState(parent[0]):
return parent[1]
if parent[0] not in explored:
explored.add(parent[0])
children = problem.getSuccessors(parent[0])
for child in children:
child_state = child[0]
child_action = child[1]
if child_state not in explored:
path_to_child = list(parent[1])
path_to_child.append(child_action)
heuristic_cost = heuristic(child_state, problem)
cost_of_actions = problem.getCostOfActions(path_to_child) #using getCostOfActions to calculate the cost of actions from beginnig to this node
total_cost = heuristic_cost + cost_of_actions
child = [child_state, path_to_child]
frontier.update(child, total_cost)
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
startState = problem.getStartState() #getting the start state
states = PriorityQueue(
) #initializing a Priorityqueue because we need to prioritize nodes/path based on the cost
t = (startState, [], 0
) #creating tuple containg state coordinated and path
states.push(t, 0) #pushed the tuple to states stack
expandedStates = [
] #inititalized an empty list to store the visited states/coordinates
while not states.isEmpty():
currState, currPath, currCost = states.pop()
#print "Cuurent State: ", currState, currPath, currCost
if problem.isGoalState(currState):
return currPath
if currState not in expandedStates:
expandedStates.append(
currState) #adding the state to expanded state list
#print "expandedStates: ", expandedStates
successors = problem.getSuccessors(currState) #finding successors
#print "successors: ", successors
for succ in successors:
succState = succ[0]
succMove = succ[1]
succCost = succ[2]
totalCost = currCost + succCost
succPath = list(currPath)
hCost = heuristic(succState, problem)
#print "totalCost: ", totalCost
#print "curPath: ", curPath
succPath.append(succMove)
states.push(
(succState, succPath, totalCost),
totalCost + hCost) #adding successors to states stack
return []
util.raiseNotDefined()
def findPathToClosestDot(self, gameState):
"Returns a path (a list of actions) to the closest dot, starting from gameState"
# Here are some useful elements of the startState
startPosition = gameState.getPacmanPosition()
food = gameState.getFood()
walls = gameState.getWalls()
problem = AnyFoodSearchProblem(gameState)
"*** YOUR CODE HERE ***"
from util import PriorityQueue
fila = PriorityQueue()
colorir = []
inicial = (startPosition, None, None, [])
"Tupla formada por (Estado atual, Estado Pai, Ação, Distancia)"
fila.push(inicial, 0)
colorir.append(inicial)
while fila.isEmpty() == False:
noAtual = fila.pop()
cord = noAtual[0]
if food[cord[0]][cord[1]] == True:
return noAtual[3]
else:
for item in problem.getSuccessors(noAtual[0]):
est = item[0]
aco = item[1]
cus = item[2]
if est not in colorir:
no1 = (est, noAtual, aco)
acoesAlcancar = qtdAcoesAlcancar(no1)
no2 = (est, noAtual, aco, acoesAlcancar)
fila.push(no2, problem.getCostOfActions(no2[3]))
colorir.append(est)
print "FOOOD", food[startPosition[0]][startPosition[1]]
def waStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has has the weighted (x 2) lowest combined cost and heuristic first."""
"*** YOUR CODE HERE FOR TASK 2 ***"
from util import PriorityQueue
from game import Directions
s = Directions.SOUTH
w = Directions.WEST
n = Directions.NORTH
e = Directions.EAST
Frontier = PriorityQueue()
closed = set()
startPoint = problem.getStartState()
gCostNow = {startPoint: 0}
fCost = {startPoint: 2 * heuristic(startPoint, problem) + 0}
actionPath = []
Frontier.push((startPoint, actionPath), fCost[startPoint])
parent = {}
while not Frontier.isEmpty():
firstNode = Frontier.pop()
currentPos = firstNode[0]
actionPath = firstNode[1]
if problem.isGoalState(currentPos):
path = [currentPos]
while path[-1] != startPoint:
parentNode = parent[path[-1]]
path.append(parentNode)
path.reverse()
#print(path)
#print(actionPath)
return actionPath
if currentPos not in closed:
closed.add(currentPos)
for neighbour in problem.getSuccessors(currentPos):
nodeState = neighbour[0]
nodeAction = neighbour[1]
if nodeState not in gCostNow.keys() and nodeState not in closed:
gCostNow[nodeState] = problem.getCostOfActions(actionPath +
[nodeAction])
fCost[nodeState] = gCostNow[nodeState] + 2 * heuristic(
nodeState, problem)
parent[nodeState] = currentPos
newUpdate(Frontier, (nodeState, actionPath + [nodeAction]),
fCost[nodeState])
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
open_list = PriorityQueue()
open_list.push([(problem.getStartState(), '', 0)], 0)
visited_nodes = []
final_path = []
while not open_list.isEmpty():
node = open_list.pop()
state = node[-1][0]
if problem.isGoalState(state):
for n in node:
if n[1] != '':
final_path.append(n[1])
return final_path
if state not in visited_nodes:
visited_nodes.append(state)
for successor in problem.getSuccessors(state):
new_path = node[:] + [successor]
new_path_actions = []
for n in node:
if n[1] != '':
new_path_actions.append(n[1])
new_path_actions.append(successor[1])
new_path_cost = problem.getCostOfActions(new_path_actions)
open_list.push(new_path, new_path_cost)
return []
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
from util import PriorityQueue
# Once again we use a priority queue, we instead use estimated cost as our priority
PriorityQueue = util.PriorityQueue()
for successor in problem.getSuccessors(problem.getStartState()):
# We calculate the estimated cost by adding our heuristic to the cost
estCost = successor[2] + heuristic(successor[0], problem)
PriorityQueue.push((successor, []), estCost)
# another searching strategy, another empty visited set
visitedSet = set()
# Much like before, this checks if a given priority queue contains a given coord
def pQueueContainsNode(PriorityQueue, coord):
for item in PriorityQueue.heap:
if item[0] == coord:
return True
return False
# this loop doesn't end unless we return
while True:
# The queue is empty, as are our hopes of finding a solution
if PriorityQueue.isEmpty():
return False
# Pop out that node and path
currentNode, currentPath = PriorityQueue.pop()
# We found the way
if problem.isGoalState(currentNode[0]):
solution = currentPath + [currentNode[1]]
# We tell pacman the way
return solution
# If we have never been there and it isn't on our fringe
if not (pQueueContainsNode(PriorityQueue, currentNode[0])) and not (
setContainsNode(visitedSet, currentNode[0])):
# Now we been there
visitedSet.add(currentNode[0])
path = currentPath + [currentNode[1]]
# Now we add the successors to the fringe, once again calculating estimated cost and using that as our priority
for successor in problem.getSuccessors(currentNode[0]):
estCost = successor[2] + heuristic(successor[0], problem)
PriorityQueue.push((successor, path), estCost)
