felixm/intro2ai
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Finish problem 2.

Browse Source
This commit is contained in:
Felix Martin 2021-11-13 17:10:02 -05:00
parent 43ad652269
commit a4031888de
4 changed files with 842 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored
View File

@ -1,2 +1,3 @@
__pycache__
*.pyc
.vscode

69
p2_multiagent/multiAgents.py
View File

@ -223,8 +223,27 @@ class ExpectimaxAgent(MultiAgentSearchAgent):
All ghosts should be modeled as choosing uniformly at random from their
legal moves.
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
numAgents = gameState.getNumAgents()
totalDepth = self.depth * numAgents
def value(depth, state):
agentIndex = depth % numAgents
actions = state.getLegalActions(agentIndex)
if not actions or depth == totalDepth:
return (self.evaluationFunction(state), "terminal")
successorStates = [state.generateSuccessor(agentIndex, action) for action in actions]
successorValueActionPairs = [(value(depth + 1, state)[0], action)
for action, state in zip(actions, successorStates)]
# Pacman (agentIndex=0) maximizes, ghosts minimize.
if agentIndex == 0:
return max(successorValueActionPairs)
else:
values = [va[0] for va in successorValueActionPairs]
average = sum(values) / float(len(values))
return (average, "expected")
# [0] is the best value, [1] is the best action
return value(0, gameState)[1]
def betterEvaluationFunction(currentGameState):
@ -234,9 +253,51 @@ def betterEvaluationFunction(currentGameState):
DESCRIPTION: <write something here so we know what you did>
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
from searchAgents import mazeDistance
state = currentGameState
pos = state.getPacmanPosition()
# foodDists = [mazeDistance(pos, foodPos, state)
# for foodPos in state.getFood().asList()]
scaredTimeScore = 0
scaredTimes = [ghostSt.scaredTimer for ghostSt in state.getGhostStates()]
if scaredTimes:
scaredTimeScore = min(scaredTimes)
ghostDists = []
for ghostState in state.getGhostStates():
x, y = ghostState.getPosition()
ghostPos = (int(x), int(y))
distance = mazeDistance(pos, ghostPos, state)
ghostDists.append(distance)
if ghostDists:
try:
ghostScore = 1. / min(ghostDists)
except ZeroDivisionError:
ghostScore = 100
foodDists = [manhattanDistance(pos, foodPos)
for foodPos in state.getFood().asList()]
foodScore = 0
if foodDists:
foodScore = 1. / min(foodDists)
gameScore = state.getScore()
weightGhost = -0.01
weightFood = 0.5
weightScore = 0.2
weightScaredTime = 0.01
score = ghostScore * weightGhost + \
foodScore * weightFood + \
gameScore * weightScore + \
scaredTimeScore * weightScaredTime
# print(state)
# print(score, ghostScore, foodScore, gameScore, scaredTimeScore)
return score
# Abbreviation
better = betterEvaluationFunction

153
p2_multiagent/search.py Normal file
View File

@ -0,0 +1,153 @@
# search.py
# ---------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
"""
In search.py, you will implement generic search algorithms which are called by
Pacman agents (in searchAgents.py).
"""
import util
class SearchProblem:
"""
This class outlines the structure of a search problem, but doesn't implement
any of the methods (in object-oriented terminology: an abstract class).
You do not need to change anything in this class, ever.
"""
def getStartState(self):
"""
Returns the start state for the search problem.
"""
util.raiseNotDefined()
def isGoalState(self, state):
"""
state: Search state
Returns True if and only if the state is a valid goal state.
"""
util.raiseNotDefined()
def getSuccessors(self, state):
"""
state: Search state
For a given state, this should return a list of triples, (successor,
action, stepCost), where 'successor' is a successor to the current
state, 'action' is the action required to get there, and 'stepCost' is
the incremental cost of expanding to that successor.
"""
util.raiseNotDefined()
def getCostOfActions(self, actions):
"""
actions: A list of actions to take
This method returns the total cost of a particular sequence of actions.
The sequence must be composed of legal moves.
"""
util.raiseNotDefined()
def tinyMazeSearch(problem):
"""
Returns a sequence of moves that solves tinyMaze. For any other maze, the
sequence of moves will be incorrect, so only use this for tinyMaze.
"""
from game import Directions
s = Directions.SOUTH
w = Directions.WEST
return [s, s, w, s, w, w, s, w]
def genericSearch(problem, getNewCostAndPriority):
fringe = util.PriorityQueue()
startState = problem.getStartState()
fringe.push((startState, [], 0), 0)
visited = {}
while True:
if fringe.isEmpty():
raise Exception("No path found.")
state, actions, cost = fringe.pop()
if problem.isGoalState(state):
return actions
if state in visited and cost >= visited[state]:
continue
visited[state] = cost
for successor, action, stepCost in problem.getSuccessors(state):
newCost, priority = getNewCostAndPriority(cost, stepCost, successor)
newActions = list(actions) + [action]
fringe.push((successor, newActions, newCost), priority)
def depthFirstSearch(problem):
"""
Search the deepest nodes in the search tree first.
Your search algorithm needs to return a list of actions that reaches the
goal. Make sure to implement a graph search algorithm.
"""
def getNewCostAndPriority(cost, stepCost, successor):
newCost = cost + 1
return newCost, -newCost
return genericSearch(problem, getNewCostAndPriority)
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
def getNewCostAndPriority(cost, stepCost, successor):
newCost = cost + 1
return newCost, newCost
return genericSearch(problem, getNewCostAndPriority)
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
def getNewCostAndPriority(cost, stepCost, successor):
newCost = cost + stepCost
return newCost, newCost
return genericSearch(problem, getNewCostAndPriority)
def nullHeuristic(state, problem=None):
"""
A heuristic function estimates the cost from the current state to the nearest
goal in the provided SearchProblem. This heuristic is trivial.
"""
return 0
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
def getNewCostAndPriority(cost, stepCost, successor):
newCost = cost + stepCost
newPriority = newCost + heuristic(successor, problem)
return newCost, newPriority
return genericSearch(problem, getNewCostAndPriority)
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch

623
p2_multiagent/searchAgents.py Normal file
View File

@ -0,0 +1,623 @@
# searchAgents.py
# ---------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
"""
This file contains all of the agents that can be selected to control Pacman. To
select an agent, use the '-p' option when running pacman.py. Arguments can be
passed to your agent using '-a'. For example, to load a SearchAgent that uses
depth first search (dfs), run the following command:
> python pacman.py -p SearchAgent -a fn=depthFirstSearch
Commands to invoke other search strategies can be found in the project
description.
Please only change the parts of the file you are asked to. Look for the lines
that say
"*** YOUR CODE HERE ***"
The parts you fill in start about 3/4 of the way down. Follow the project
description for details.
Good luck and happy searching!
"""
from game import Directions
from game import Agent
from game import Actions
import util
import time
import search
class GoWestAgent(Agent):
"An agent that goes West until it can't."
def getAction(self, state):
"The agent receives a GameState (defined in pacman.py)."
if Directions.WEST in state.getLegalPacmanActions():
return Directions.WEST
else:
return Directions.STOP
#######################################################
# This portion is written for you, but will only work #
# after you fill in parts of search.py #
#######################################################
class SearchAgent(Agent):
"""
This very general search agent finds a path using a supplied search
algorithm for a supplied search problem, then returns actions to follow that
path.
As a default, this agent runs DFS on a PositionSearchProblem to find
location (1,1)
Options for fn include:
depthFirstSearch or dfs
breadthFirstSearch or bfs
Note: You should NOT change any code in SearchAgent
"""
def __init__(self, fn='depthFirstSearch', prob='PositionSearchProblem', heuristic='nullHeuristic'):
# Warning: some advanced Python magic is employed below to find the right functions and problems
# Get the search function from the name and heuristic
if fn not in dir(search):
raise AttributeError, fn + ' is not a search function in search.py.'
func = getattr(search, fn)
if 'heuristic' not in func.func_code.co_varnames:
print('[SearchAgent] using function ' + fn)
self.searchFunction = func
else:
if heuristic in globals().keys():
heur = globals()[heuristic]
elif heuristic in dir(search):
heur = getattr(search, heuristic)
else:
raise AttributeError, heuristic + ' is not a function in searchAgents.py or search.py.'
print('[SearchAgent] using function %s and heuristic %s' %
(fn, heuristic))
# Note: this bit of Python trickery combines the search algorithm and the heuristic
self.searchFunction = lambda x: func(x, heuristic=heur)
# Get the search problem type from the name
if prob not in globals().keys() or not prob.endswith('Problem'):
raise AttributeError, prob + ' is not a search problem type in SearchAgents.py.'
self.searchType = globals()[prob]
print('[SearchAgent] using problem type ' + prob)
def registerInitialState(self, state):
"""
This is the first time that the agent sees the layout of the game
board. Here, we choose a path to the goal. In this phase, the agent
should compute the path to the goal and store it in a local variable.
All of the work is done in this method!
state: a GameState object (pacman.py)
"""
if self.searchFunction == None:
raise Exception, "No search function provided for SearchAgent"
starttime = time.time()
problem = self.searchType(state) # Makes a new search problem
self.actions = self.searchFunction(problem) # Find a path
totalCost = problem.getCostOfActions(self.actions)
print('Path found with total cost of %d in %.1f seconds' %
(totalCost, time.time() - starttime))
if '_expanded' in dir(problem):
print('Search nodes expanded: %d' % problem._expanded)
def getAction(self, state):
"""
Returns the next action in the path chosen earlier (in
registerInitialState). Return Directions.STOP if there is no further
action to take.
state: a GameState object (pacman.py)
"""
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
class PositionSearchProblem(search.SearchProblem):
"""
A search problem defines the state space, start state, goal test, successor
function and cost function. This search problem can be used to find paths
to a particular point on the pacman board.
The state space consists of (x,y) positions in a pacman game.
Note: this search problem is fully specified; you should NOT change it.
"""
def __init__(self, gameState, costFn=lambda x: 1, goal=(1, 1), start=None, warn=True, visualize=True):
"""
Stores the start and goal.
gameState: A GameState object (pacman.py)
costFn: A function from a search state (tuple) to a non-negative number
goal: A position in the gameState
"""
self.walls = gameState.getWalls()
self.startState = gameState.getPacmanPosition()
if start != None:
self.startState = start
self.goal = goal
self.costFn = costFn
self.visualize = visualize
if warn and (gameState.getNumFood() != 1 or not gameState.hasFood(*goal)):
print 'Warning: this does not look like a regular search maze'
# For display purposes
self._visited, self._visitedlist, self._expanded = {}, [], 0 # DO NOT CHANGE
def getStartState(self):
return self.startState
def isGoalState(self, state):
isGoal = state == self.goal
# For display purposes only
if isGoal and self.visualize:
self._visitedlist.append(state)
import __main__
if '_display' in dir(__main__):
# @UndefinedVariable
if 'drawExpandedCells' in dir(__main__._display):
__main__._display.drawExpandedCells(
self._visitedlist) # @UndefinedVariable
return isGoal
def getSuccessors(self, state):
"""
Returns successor states, the actions they require, and a cost of 1.
As noted in search.py:
For a given state, this should return a list of triples,
(successor, action, stepCost), where 'successor' is a
successor to the current state, 'action' is the action
required to get there, and 'stepCost' is the incremental
cost of expanding to that successor
"""
successors = []
for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:
x, y = state
dx, dy = Actions.directionToVector(action)
nextx, nexty = int(x + dx), int(y + dy)
if not self.walls[nextx][nexty]:
nextState = (nextx, nexty)
cost = self.costFn(nextState)
successors.append((nextState, action, cost))
# Bookkeeping for display purposes
self._expanded += 1 # DO NOT CHANGE
if state not in self._visited:
self._visited[state] = True
self._visitedlist.append(state)
return successors
def getCostOfActions(self, actions):
"""
Returns the cost of a particular sequence of actions. If those actions
include an illegal move, return 999999.
"""
if actions == None:
return 999999
x, y = self.getStartState()
cost = 0
for action in actions:
# Check figure out the next state and see whether its' legal
dx, dy = Actions.directionToVector(action)
x, y = int(x + dx), int(y + dy)
if self.walls[x][y]:
return 999999
cost += self.costFn((x, y))
return cost
class StayEastSearchAgent(SearchAgent):
"""
An agent for position search with a cost function that penalizes being in
positions on the West side of the board.
The cost function for stepping into a position (x,y) is 1/2^x.
"""
def __init__(self):
self.searchFunction = search.uniformCostSearch
def costFn(pos): return .5 ** pos[0]
self.searchType = lambda state: PositionSearchProblem(
state, costFn, (1, 1), None, False)
class StayWestSearchAgent(SearchAgent):
"""
An agent for position search with a cost function that penalizes being in
positions on the East side of the board.
The cost function for stepping into a position (x,y) is 2^x.
"""
def __init__(self):
self.searchFunction = search.uniformCostSearch
def costFn(pos): return 2 ** pos[0]
self.searchType = lambda state: PositionSearchProblem(state, costFn)
def manhattanHeuristic(position, problem, info={}):
"The Manhattan distance heuristic for a PositionSearchProblem"
xy1 = position
xy2 = problem.goal
return abs(xy1[0] - xy2[0]) + abs(xy1[1] - xy2[1])
def euclideanHeuristic(position, problem, info={}):
"The Euclidean distance heuristic for a PositionSearchProblem"
xy1 = position
xy2 = problem.goal
return ((xy1[0] - xy2[0]) ** 2 + (xy1[1] - xy2[1]) ** 2) ** 0.5
#####################################################
# This portion is incomplete. Time to write code! #
#####################################################
class CornersProblem(search.SearchProblem):
"""
This search problem finds paths through all four corners of a layout.
You must select a suitable state space and successor function
"""
def __init__(self, startingGameState):
"""
Stores the walls, pacman's starting position and corners.
"""
self.walls = startingGameState.getWalls()
self.startingPosition = startingGameState.getPacmanPosition()
top, right = self.walls.height-2, self.walls.width-2
self.corners = ((1, 1), (1, top), (right, 1), (right, top))
for corner in self.corners:
if not startingGameState.hasFood(*corner):
print 'Warning: no food in corner ' + str(corner)
self._expanded = 0 # DO NOT CHANGE; Number of search nodes expanded
# Please add any code here which you would like to use
# in initializing the problem
def getStartState(self):
"""
Returns the start state (in your state space, not the full Pacman state
space)
"""
current = self.startingPosition
visited = tuple([1 if corner == current else 0
for corner in self.corners])
return (self.startingPosition, visited)
def isGoalState(self, state):
"""
Returns whether this search state is a goal state of the problem.
"""
"*** YOUR CODE HERE ***"
position, visited = state
if sum(visited) == 4:
return True
return False
def getSuccessors(self, state):
"""
Returns successor states, the actions they require, and a cost of 1.
As noted in search.py:
For a given state, this should return a list of triples, (successor,
action, stepCost), where 'successor' is a successor to the current
state, 'action' is the action required to get there, and 'stepCost'
is the incremental cost of expanding to that successor
"""
position, visited = state
x, y = position
successors = []
options = [((x, y + 1), Directions.NORTH),
((x, y - 1), Directions.SOUTH),
((x + 1, y), Directions.EAST),
((x - 1, y), Directions.WEST)]
for newPosition, action in options:
x, y = newPosition
if self.walls[x][y]:
continue
if newPosition in self.corners:
index = self.corners.index(newPosition)
newVisited = list(visited)
newVisited[index] = 1
newVisited = tuple(newVisited)
else:
newVisited = visited
newState = (newPosition, newVisited)
successors.append((newState, action, 1))
self._expanded += 1 # DO NOT CHANGE
return successors
def getCostOfActions(self, actions):
"""
Returns the cost of a particular sequence of actions. If those actions
include an illegal move, return 999999. This is implemented for you.
"""
if actions == None:
return 999999
x, y = self.startingPosition
for action in actions:
dx, dy = Actions.directionToVector(action)
x, y = int(x + dx), int(y + dy)
if self.walls[x][y]:
return 999999
return len(actions)
def cornersHeuristic(state, problem):
"""
A heuristic for the CornersProblem that you defined.
state: The current search state
(a data structure you chose in your search problem)
problem: The CornersProblem instance for this layout.
This function should always return a number that is a lower bound on the
shortest path from the state to a goal of the problem; i.e. it should be
admissible (as well as consistent).
"""
corners = problem.corners # These are the corner coordinates
position, visitedCorners = state
# self.corners = ((1, 1), (1, top), (right, 1), (right, top))
minDist = min(corners[2][0] - 1, corners[1][1] - 1)
# Okay, I am having a way harder time with this than I should.
# First, get only the corners Pacman hasn't visited yet.
distToCorners = [util.manhattanDistance(position, corner)
for corner, visited in zip(corners, visitedCorners)
if visited == 0]
# If there are no corners left, we are done.
if not distToCorners:
return 0
distanceClosestCorner = min(distToCorners)
cost = distanceClosestCorner + (len(distToCorners) - 1) * minDist
return cost
class AStarCornersAgent(SearchAgent):
"A SearchAgent for FoodSearchProblem using A* and your foodHeuristic"
def __init__(self):
self.searchFunction = lambda prob: search.aStarSearch(
prob, cornersHeuristic)
self.searchType = CornersProblem
class FoodSearchProblem:
"""
A search problem associated with finding the a path that collects all of the
food (dots) in a Pacman game.
A search state in this problem is a tuple ( pacmanPosition, foodGrid ) where
pacmanPosition: a tuple (x,y) of integers specifying Pacman's position
foodGrid: a Grid (see game.py) of either True or False, specifying remaining food
"""
def __init__(self, startingGameState):
self.start = (startingGameState.getPacmanPosition(),
startingGameState.getFood())
self.walls = startingGameState.getWalls()
self.startingGameState = startingGameState
self._expanded = 0 # DO NOT CHANGE
self.heuristicInfo = {} # A dictionary for the heuristic to store information
def getStartState(self):
return self.start
def isGoalState(self, state):
return state[1].count() == 0
def getSuccessors(self, state):
"Returns successor states, the actions they require, and a cost of 1."
successors = []
self._expanded += 1 # DO NOT CHANGE
for direction in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:
x, y = state[0]
dx, dy = Actions.directionToVector(direction)
nextx, nexty = int(x + dx), int(y + dy)
if not self.walls[nextx][nexty]:
nextFood = state[1].copy()
nextFood[nextx][nexty] = False
successors.append((((nextx, nexty), nextFood), direction, 1))
return successors
def getCostOfActions(self, actions):
"""Returns the cost of a particular sequence of actions. If those actions
include an illegal move, return 999999"""
x, y = self.getStartState()[0]
cost = 0
for action in actions:
# figure out the next state and see whether it's legal
dx, dy = Actions.directionToVector(action)
x, y = int(x + dx), int(y + dy)
if self.walls[x][y]:
return 999999
cost += 1
return cost
class AStarFoodSearchAgent(SearchAgent):
"A SearchAgent for FoodSearchProblem using A* and your foodHeuristic"
def __init__(self):
self.searchFunction = lambda prob: search.aStarSearch(
prob, foodHeuristic)
self.searchType = FoodSearchProblem
def foodHeuristic(state, problem):
"""
Your heuristic for the FoodSearchProblem goes here.
This heuristic must be consistent to ensure correctness. First, try to come
up with an admissible heuristic; almost all admissible heuristics will be
consistent as well.
If using A* ever finds a solution that is worse uniform cost search finds,
your heuristic is *not* consistent, and probably not admissible! On the
other hand, inadmissible or inconsistent heuristics may find optimal
solutions, so be careful.
The state is a tuple ( pacmanPosition, foodGrid ) where foodGrid is a Grid
(see game.py) of either True or False. You can call foodGrid.asList() to get
a list of food coordinates instead.
If you want access to info like walls, capsules, etc., you can query the
problem. For example, problem.walls gives you a Grid of where the walls
are.
If you want to *store* information to be reused in other calls to the
heuristic, there is a dictionary called problem.heuristicInfo that you can
use. For example, if you only want to count the walls once and store that
value, try: problem.heuristicInfo['wallCount'] = problem.walls.count()
Subsequent calls to this heuristic can access
problem.heuristicInfo['wallCount']
"""
position, foodGrid = state
foodPositions = foodGrid.asList()
if not foodPositions:
return 0
# We have to travel at least from x_min to x_max and y_min to y_max.
foodX = [x for (x, y) in foodPositions]
foodY = [y for (x, y) in foodPositions]
cost = (max(foodX) - min(foodX)) + (max(foodY) - min(foodY))
# The previous gave over 9000 for trickySearch. We can improve by adding
# the distance to the closest food position which gives over 7000 points.
cost += min([util.manhattanDistance(position, foodPosition)
for foodPosition in foodPositions])
# If I wanted to get full score, I would use the cost to the closest food,
# plus a TSP from there. That would give us less than 7000 for sure.
return cost
class ClosestDotSearchAgent(SearchAgent):
"Search for all food using a sequence of searches"
def registerInitialState(self, state):
self.actions = []
currentState = state
while(currentState.getFood().count() > 0):
nextPathSegment = self.findPathToClosestDot(
currentState) # The missing piece
self.actions += nextPathSegment
for action in nextPathSegment:
legal = currentState.getLegalActions()
if action not in legal:
t = (str(action), str(currentState))
raise Exception, 'findPathToClosestDot returned an illegal move: %s!\n%s' % t
currentState = currentState.generateSuccessor(0, action)
self.actionIndex = 0
print 'Path found with cost %d.' % len(self.actions)
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)
return search.ucs(problem)
class AnyFoodSearchProblem(PositionSearchProblem):
"""
A search problem for finding a path to any food.
This search problem is just like the PositionSearchProblem, but has a
different goal test, which you need to fill in below. The state space and
successor function do not need to be changed.
The class definition above, AnyFoodSearchProblem(PositionSearchProblem),
inherits the methods of the PositionSearchProblem.
You can use this search problem to help you fill in the findPathToClosestDot
method.
"""
def __init__(self, gameState):
"Stores information from the gameState. You don't need to change this."
# Store the food for later reference
self.food = gameState.getFood()
# Store info for the PositionSearchProblem (no need to change this)
self.walls = gameState.getWalls()
self.startState = gameState.getPacmanPosition()
self.costFn = lambda x: 1
self._visited, self._visitedlist, self._expanded = {}, [], 0 # DO NOT CHANGE
def isGoalState(self, state):
"""
The state is Pacman's position. Fill this in with a goal test that will
complete the problem definition.
"""
x, y = state
if (x, y) in self.food.asList():
return True
return False
def mazeDistance(point1, point2, gameState):
"""
Returns the maze distance between any two points, using the search functions
you have already built. The gameState can be any game state -- Pacman's
position in that state is ignored.
Example usage: mazeDistance( (2,4), (5,6), gameState)
This might be a useful helper function for your ApproximateSearchAgent.
"""
x1, y1 = point1
x2, y2 = point2
walls = gameState.getWalls()
assert not walls[x1][y1], 'point1 is a wall: ' + str(point1)
assert not walls[x2][y2], 'point2 is a wall: ' + str(point2)
prob = PositionSearchProblem(
gameState, start=point1, goal=point2, warn=False, visualize=False)
return len(search.bfs(prob))