# 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__):
? ? ? ? ? ? ? ? if 'drawExpandedCells' in dir(__main__._display): #@UndefinedVariable
? ? ? ? ? ? ? ? ? ? __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
? ? ? ? costFn = lambda pos: .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
? ? ? ? costFn = lambda pos: 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
? ? ? ? "*** YOUR CODE HERE ***"
? ? def getStartState(self):
? ? ? ? """
? ? ? ? Returns the start state (in your state space, not the full Pacman state
? ? ? ? space)
? ? ? ? """
? ? ? ? "*** YOUR CODE HERE ***"
? ? ? ? util.raiseNotDefined()
? ? def isGoalState(self, state):
? ? ? ? """
? ? ? ? Returns whether this search state is a goal state of the problem.
? ? ? ? """
? ? ? ? "*** YOUR CODE HERE ***"
? ? ? ? util.raiseNotDefined()
? ? 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]:
? ? ? ? ? ? # Add a successor state to the successor list if the action is legal
? ? ? ? ? ? # Here's a code snippet for figuring out whether a new position hits a wall:
? ? ? ? ? ? #? x,y = currentPosition
? ? ? ? ? ? #? dx, dy = Actions.directionToVector(action)
? ? ? ? ? ? #? nextx, nexty = int(x + dx), int(y + dy)
? ? ? ? ? ? #? hitsWall = self.walls[nextx][nexty]
? ? ? ? ? ? "*** YOUR CODE HERE ***"
? ? ? ? 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
? ? walls = problem.walls # These are the walls of the maze, as a Grid (game.py)
? ? "*** YOUR CODE HERE ***"
? ? return 0 # Default to trivial solution
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
? ? "*** YOUR CODE HERE ***"
? ? return 0
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)
? ? ? ? "*** YOUR CODE HERE ***"
? ? ? ? util.raiseNotDefined()
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
? ? ? ? "*** YOUR CODE HERE ***"
? ? ? ? util.raiseNotDefined()
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))
