Initial commit: Pacman AI search project

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# eightpuzzle.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).
import search
import random
# Module Classes
class EightPuzzleState:
"""
The Eight Puzzle is described in the course textbook on
page 64.
This class defines the mechanics of the puzzle itself. The
task of recasting this puzzle as a search problem is left to
the EightPuzzleSearchProblem class.
"""
def __init__( self, numbers ):
"""
Constructs a new eight puzzle from an ordering of numbers.
numbers: a list of integers from 0 to 8 representing an
instance of the eight puzzle. 0 represents the blank
space. Thus, the list
[1, 0, 2, 3, 4, 5, 6, 7, 8]
represents the eight puzzle:
-------------
| 1 | | 2 |
-------------
| 3 | 4 | 5 |
-------------
| 6 | 7 | 8 |
------------
The configuration of the puzzle is stored in a 2-dimensional
list (a list of lists) 'cells'.
"""
self.cells = []
numbers = numbers[:] # Make a copy so as not to cause side-effects.
numbers.reverse()
for row in range( 3 ):
self.cells.append( [] )
for col in range( 3 ):
self.cells[row].append( numbers.pop() )
if self.cells[row][col] == 0:
self.blankLocation = row, col
def isGoal( self ):
"""
Checks to see if the puzzle is in its goal state.
-------------
| | 1 | 2 |
-------------
| 3 | 4 | 5 |
-------------
| 6 | 7 | 8 |
-------------
>>> EightPuzzleState([0, 1, 2, 3, 4, 5, 6, 7, 8]).isGoal()
True
>>> EightPuzzleState([1, 0, 2, 3, 4, 5, 6, 7, 8]).isGoal()
False
"""
current = 0
for row in range( 3 ):
for col in range( 3 ):
if current != self.cells[row][col]:
return False
current += 1
return True
def legalMoves( self ):
"""
Returns a list of legal moves from the current state.
Moves consist of moving the blank space up, down, left or right.
These are encoded as 'up', 'down', 'left' and 'right' respectively.
>>> EightPuzzleState([0, 1, 2, 3, 4, 5, 6, 7, 8]).legalMoves()
['down', 'right']
"""
moves = []
row, col = self.blankLocation
if(row != 0):
moves.append('up')
if(row != 2):
moves.append('down')
if(col != 0):
moves.append('left')
if(col != 2):
moves.append('right')
return moves
def result(self, move):
"""
Returns a new eightPuzzle with the current state and blankLocation
updated based on the provided move.
The move should be a string drawn from a list returned by legalMoves.
Illegal moves will raise an exception, which may be an array bounds
exception.
NOTE: This function *does not* change the current object. Instead,
it returns a new object.
"""
row, col = self.blankLocation
if(move == 'up'):
newrow = row - 1
newcol = col
elif(move == 'down'):
newrow = row + 1
newcol = col
elif(move == 'left'):
newrow = row
newcol = col - 1
elif(move == 'right'):
newrow = row
newcol = col + 1
else:
raise "Illegal Move"
# Create a copy of the current eightPuzzle
newPuzzle = EightPuzzleState([0, 0, 0, 0, 0, 0, 0, 0, 0])
newPuzzle.cells = [values[:] for values in self.cells]
# And update it to reflect the move
newPuzzle.cells[row][col] = self.cells[newrow][newcol]
newPuzzle.cells[newrow][newcol] = self.cells[row][col]
newPuzzle.blankLocation = newrow, newcol
return newPuzzle
# Utilities for comparison and display
def __eq__(self, other):
"""
Overloads '==' such that two eightPuzzles with the same configuration
are equal.
>>> EightPuzzleState([0, 1, 2, 3, 4, 5, 6, 7, 8]) == \
EightPuzzleState([1, 0, 2, 3, 4, 5, 6, 7, 8]).result('left')
True
"""
for row in range( 3 ):
if self.cells[row] != other.cells[row]:
return False
return True
def __hash__(self):
return hash(str(self.cells))
def __getAsciiString(self):
"""
Returns a display string for the maze
"""
lines = []
horizontalLine = ('-' * (13))
lines.append(horizontalLine)
for row in self.cells:
rowLine = '|'
for col in row:
if col == 0:
col = ' '
rowLine = rowLine + ' ' + col.__str__() + ' |'
lines.append(rowLine)
lines.append(horizontalLine)
return '\n'.join(lines)
def __str__(self):
return self.__getAsciiString()
# TODO: Implement The methods in this class
class EightPuzzleSearchProblem(search.SearchProblem):
"""
Implementation of a SearchProblem for the Eight Puzzle domain
Each state is represented by an instance of an eightPuzzle.
"""
def __init__(self,puzzle):
"Creates a new EightPuzzleSearchProblem which stores search information."
self.puzzle = puzzle
def getStartState(self):
return puzzle
def isGoalState(self,state):
return state.isGoal()
def getSuccessors(self,state):
"""
Returns list of (successor, action, stepCost) pairs where
each succesor is either left, right, up, or down
from the original state and the cost is 1.0 for each
"""
succ = []
for a in state.legalMoves():
succ.append((state.result(a), a, 1))
return succ
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
"""
return len(actions)
EIGHT_PUZZLE_DATA = [[1, 0, 2, 3, 4, 5, 6, 7, 8],
[1, 7, 8, 2, 3, 4, 5, 6, 0],
[4, 3, 2, 7, 0, 5, 1, 6, 8],
[5, 1, 3, 4, 0, 2, 6, 7, 8],
[1, 2, 5, 7, 6, 8, 0, 4, 3],
[0, 3, 1, 6, 8, 2, 7, 5, 4]]
def loadEightPuzzle(puzzleNumber):
"""
puzzleNumber: The number of the eight puzzle to load.
Returns an eight puzzle object generated from one of the
provided puzzles in EIGHT_PUZZLE_DATA.
puzzleNumber can range from 0 to 5.
>>> print(loadEightPuzzle(0))
-------------
| 1 | | 2 |
-------------
| 3 | 4 | 5 |
-------------
| 6 | 7 | 8 |
-------------
"""
return EightPuzzleState(EIGHT_PUZZLE_DATA[puzzleNumber])
def createRandomEightPuzzle(moves=100):
"""
moves: number of random moves to apply
Creates a random eight puzzle by applying
a series of 'moves' random moves to a solved
puzzle.
"""
puzzle = EightPuzzleState([0,1,2,3,4,5,6,7,8])
for i in range(moves):
# Execute a random legal move
puzzle = puzzle.result(random.sample(puzzle.legalMoves(), 1)[0])
return puzzle
if __name__ == '__main__':
puzzle = createRandomEightPuzzle(25)
print('A random puzzle:')
print(puzzle)
problem = EightPuzzleSearchProblem(puzzle)
path = search.breadthFirstSearch(problem)
print('BFS found a path of %d moves: %s' % (len(path), str(path)))
curr = puzzle
i = 1
for a in path:
curr = curr.result(a)
print('After %d move%s: %s' % (i, ("", "s")[i>1], a))
print(curr)
input("Press return for the next state...") # wait for key stroke
i += 1
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# game.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).
# game.py
# -------
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html
from util import *
import time, os
import traceback
import sys
#######################
# Parts worth reading #
#######################
class Agent:
"""
An agent must define a getAction method, but may also define the
following methods which will be called if they exist:
def registerInitialState(self, state): # inspects the starting state
"""
def __init__(self, index=0):
self.index = index
def getAction(self, state):
"""
The Agent will receive a GameState (from either {pacman, capture, sonar}.py) and
must return an action from Directions.{North, South, East, West, Stop}
"""
raiseNotDefined()
class Directions:
NORTH = 'North'
SOUTH = 'South'
EAST = 'East'
WEST = 'West'
STOP = 'Stop'
LEFT = {NORTH: WEST,
SOUTH: EAST,
EAST: NORTH,
WEST: SOUTH,
STOP: STOP}
RIGHT = dict([(y,x) for x, y in LEFT.items()])
REVERSE = {NORTH: SOUTH,
SOUTH: NORTH,
EAST: WEST,
WEST: EAST,
STOP: STOP}
class Configuration:
"""
A Configuration holds the (x,y) coordinate of a character, along with its
traveling direction.
The convention for positions, like a graph, is that (0,0) is the lower left corner, x increases
horizontally and y increases vertically. Therefore, north is the direction of increasing y, or (0,1).
"""
def __init__(self, pos, direction):
self.pos = pos
self.direction = direction
def getPosition(self):
return (self.pos)
def getDirection(self):
return self.direction
def isInteger(self):
x,y = self.pos
return x == int(x) and y == int(y)
def __eq__(self, other):
if other == None: return False
return (self.pos == other.pos and self.direction == other.direction)
def __hash__(self):
x = hash(self.pos)
y = hash(self.direction)
return hash(x + 13 * y)
def __str__(self):
return "(x,y)="+str(self.pos)+", "+str(self.direction)
def generateSuccessor(self, vector):
"""
Generates a new configuration reached by translating the current
configuration by the action vector. This is a low-level call and does
not attempt to respect the legality of the movement.
Actions are movement vectors.
"""
x, y= self.pos
dx, dy = vector
direction = Actions.vectorToDirection(vector)
if direction == Directions.STOP:
direction = self.direction # There is no stop direction
return Configuration((x + dx, y+dy), direction)
class AgentState:
"""
AgentStates hold the state of an agent (configuration, speed, scared, etc).
"""
def __init__( self, startConfiguration, isPacman ):
self.start = startConfiguration
self.configuration = startConfiguration
self.isPacman = isPacman
self.scaredTimer = 0
self.numCarrying = 0
self.numReturned = 0
def __str__( self ):
if self.isPacman:
return "Pacman: " + str( self.configuration )
else:
return "Ghost: " + str( self.configuration )
def __eq__( self, other ):
if other == None:
return False
return self.configuration == other.configuration and self.scaredTimer == other.scaredTimer
def __hash__(self):
return hash(hash(self.configuration) + 13 * hash(self.scaredTimer))
def copy( self ):
state = AgentState( self.start, self.isPacman )
state.configuration = self.configuration
state.scaredTimer = self.scaredTimer
state.numCarrying = self.numCarrying
state.numReturned = self.numReturned
return state
def getPosition(self):
if self.configuration == None: return None
return self.configuration.getPosition()
def getDirection(self):
return self.configuration.getDirection()
class Grid:
"""
A 2-dimensional array of objects backed by a list of lists. Data is accessed
via grid[x][y] where (x,y) are positions on a Pacman map with x horizontal,
y vertical and the origin (0,0) in the bottom left corner.
The __str__ method constructs an output that is oriented like a pacman board.
"""
def __init__(self, width, height, initialValue=False, bitRepresentation=None):
if initialValue not in [False, True]: raise Exception('Grids can only contain booleans')
self.CELLS_PER_INT = 30
self.width = width
self.height = height
self.data = [[initialValue for y in range(height)] for x in range(width)]
if bitRepresentation:
self._unpackBits(bitRepresentation)
def __getitem__(self, i):
return self.data[i]
def __setitem__(self, key, item):
self.data[key] = item
def __str__(self):
out = [[str(self.data[x][y])[0] for x in range(self.width)] for y in range(self.height)]
out.reverse()
return '\n'.join([''.join(x) for x in out])
def __eq__(self, other):
if other == None: return False
return self.data == other.data
def __hash__(self):
# return hash(str(self))
base = 1
h = 0
for l in self.data:
for i in l:
if i:
h += base
base *= 2
return hash(h)
def copy(self):
g = Grid(self.width, self.height)
g.data = [x[:] for x in self.data]
return g
def deepCopy(self):
return self.copy()
def shallowCopy(self):
g = Grid(self.width, self.height)
g.data = self.data
return g
def count(self, item =True ):
return sum([x.count(item) for x in self.data])
def asList(self, key = True):
list = []
for x in range(self.width):
for y in range(self.height):
if self[x][y] == key: list.append( (x,y) )
return list
def packBits(self):
"""
Returns an efficient int list representation
(width, height, bitPackedInts...)
"""
bits = [self.width, self.height]
currentInt = 0
for i in range(self.height * self.width):
bit = self.CELLS_PER_INT - (i % self.CELLS_PER_INT) - 1
x, y = self._cellIndexToPosition(i)
if self[x][y]:
currentInt += 2 ** bit
if (i + 1) % self.CELLS_PER_INT == 0:
bits.append(currentInt)
currentInt = 0
bits.append(currentInt)
return tuple(bits)
def _cellIndexToPosition(self, index):
x = index // self.height
y = index % self.height
return x, y
def _unpackBits(self, bits):
"""
Fills in data from a bit-level representation
"""
cell = 0
for packed in bits:
for bit in self._unpackInt(packed, self.CELLS_PER_INT):
if cell == self.width * self.height: break
x, y = self._cellIndexToPosition(cell)
self[x][y] = bit
cell += 1
def _unpackInt(self, packed, size):
bools = []
if packed < 0: raise ValueError("must be a positive integer")
for i in range(size):
n = 2 ** (self.CELLS_PER_INT - i - 1)
if packed >= n:
bools.append(True)
packed -= n
else:
bools.append(False)
return bools
def reconstituteGrid(bitRep):
if type(bitRep) is not type((1,2)):
return bitRep
width, height = bitRep[:2]
return Grid(width, height, bitRepresentation= bitRep[2:])
####################################
# Parts you shouldn't have to read #
####################################
class Actions:
"""
A collection of static methods for manipulating move actions.
"""
# Directions
_directions = {Directions.NORTH: (0, 1),
Directions.SOUTH: (0, -1),
Directions.EAST: (1, 0),
Directions.WEST: (-1, 0),
Directions.STOP: (0, 0)}
_directionsAsList = _directions.items()
TOLERANCE = .001
def reverseDirection(action):
if action == Directions.NORTH:
return Directions.SOUTH
if action == Directions.SOUTH:
return Directions.NORTH
if action == Directions.EAST:
return Directions.WEST
if action == Directions.WEST:
return Directions.EAST
return action
reverseDirection = staticmethod(reverseDirection)
def vectorToDirection(vector):
dx, dy = vector
if dy > 0:
return Directions.NORTH
if dy < 0:
return Directions.SOUTH
if dx < 0:
return Directions.WEST
if dx > 0:
return Directions.EAST
return Directions.STOP
vectorToDirection = staticmethod(vectorToDirection)
def directionToVector(direction, speed = 1.0):
dx, dy = Actions._directions[direction]
return (dx * speed, dy * speed)
directionToVector = staticmethod(directionToVector)
def getPossibleActions(config, walls):
possible = []
x, y = config.pos
x_int, y_int = int(x + 0.5), int(y + 0.5)
# In between grid points, all agents must continue straight
if (abs(x - x_int) + abs(y - y_int) > Actions.TOLERANCE):
return [config.getDirection()]
for dir, vec in Actions._directionsAsList:
dx, dy = vec
next_y = y_int + dy
next_x = x_int + dx
if not walls[next_x][next_y]: possible.append(dir)
return possible
getPossibleActions = staticmethod(getPossibleActions)
def getLegalNeighbors(position, walls):
x,y = position
x_int, y_int = int(x + 0.5), int(y + 0.5)
neighbors = []
for dir, vec in Actions._directionsAsList:
dx, dy = vec
next_x = x_int + dx
if next_x < 0 or next_x == walls.width: continue
next_y = y_int + dy
if next_y < 0 or next_y == walls.height: continue
if not walls[next_x][next_y]: neighbors.append((next_x, next_y))
return neighbors
getLegalNeighbors = staticmethod(getLegalNeighbors)
def getSuccessor(position, action):
dx, dy = Actions.directionToVector(action)
x, y = position
return (x + dx, y + dy)
getSuccessor = staticmethod(getSuccessor)
class GameStateData:
"""
"""
def __init__( self, prevState = None ):
"""
Generates a new data packet by copying information from its predecessor.
"""
if prevState != None:
self.food = prevState.food.shallowCopy()
self.capsules = prevState.capsules[:]
self.agentStates = self.copyAgentStates( prevState.agentStates )
self.layout = prevState.layout
self._eaten = prevState._eaten
self.score = prevState.score
self._foodEaten = None
self._foodAdded = None
self._capsuleEaten = None
self._agentMoved = None
self._lose = False
self._win = False
self.scoreChange = 0
def deepCopy( self ):
state = GameStateData( self )
state.food = self.food.deepCopy()
state.layout = self.layout.deepCopy()
state._agentMoved = self._agentMoved
state._foodEaten = self._foodEaten
state._foodAdded = self._foodAdded
state._capsuleEaten = self._capsuleEaten
return state
def copyAgentStates( self, agentStates ):
copiedStates = []
for agentState in agentStates:
copiedStates.append( agentState.copy() )
return copiedStates
def __eq__( self, other ):
"""
Allows two states to be compared.
"""
if other == None: return False
# TODO Check for type of other
if not self.agentStates == other.agentStates: return False
if not self.food == other.food: return False
if not self.capsules == other.capsules: return False
if not self.score == other.score: return False
return True
def __hash__( self ):
"""
Allows states to be keys of dictionaries.
"""
for i, state in enumerate( self.agentStates ):
try:
int(hash(state))
except TypeError as e:
print(e)
#hash(state)
return int((hash(tuple(self.agentStates)) + 13*hash(self.food) + 113* hash(tuple(self.capsules)) + 7 * hash(self.score)) % 1048575 )
def __str__( self ):
width, height = self.layout.width, self.layout.height
map = Grid(width, height)
if type(self.food) == type((1,2)):
self.food = reconstituteGrid(self.food)
for x in range(width):
for y in range(height):
food, walls = self.food, self.layout.walls
map[x][y] = self._foodWallStr(food[x][y], walls[x][y])
for agentState in self.agentStates:
if agentState == None: continue
if agentState.configuration == None: continue
x,y = [int( i ) for i in nearestPoint( agentState.configuration.pos )]
agent_dir = agentState.configuration.direction
if agentState.isPacman:
map[x][y] = self._pacStr( agent_dir )
else:
map[x][y] = self._ghostStr( agent_dir )
for x, y in self.capsules:
map[x][y] = 'o'
return str(map) + ("\nScore: %d\n" % self.score)
def _foodWallStr( self, hasFood, hasWall ):
if hasFood:
return '.'
elif hasWall:
return '%'
else:
return ' '
def _pacStr( self, dir ):
if dir == Directions.NORTH:
return 'v'
if dir == Directions.SOUTH:
return '^'
if dir == Directions.WEST:
return '>'
return '<'
def _ghostStr( self, dir ):
return 'G'
if dir == Directions.NORTH:
return 'M'
if dir == Directions.SOUTH:
return 'W'
if dir == Directions.WEST:
return '3'
return 'E'
def initialize( self, layout, numGhostAgents ):
"""
Creates an initial game state from a layout array (see layout.py).
"""
self.food = layout.food.copy()
#self.capsules = []
self.capsules = layout.capsules[:]
self.layout = layout
self.score = 0
self.scoreChange = 0
self.agentStates = []
numGhosts = 0
for isPacman, pos in layout.agentPositions:
if not isPacman:
if numGhosts == numGhostAgents: continue # Max ghosts reached already
else: numGhosts += 1
self.agentStates.append( AgentState( Configuration( pos, Directions.STOP), isPacman) )
self._eaten = [False for a in self.agentStates]
try:
import boinc
_BOINC_ENABLED = True
except:
_BOINC_ENABLED = False
class Game:
"""
The Game manages the control flow, soliciting actions from agents.
"""
def __init__( self, agents, display, rules, startingIndex=0, muteAgents=False, catchExceptions=False ):
self.agentCrashed = False
self.agents = agents
self.display = display
self.rules = rules
self.startingIndex = startingIndex
self.gameOver = False
self.muteAgents = muteAgents
self.catchExceptions = catchExceptions
self.moveHistory = []
self.totalAgentTimes = [0 for agent in agents]
self.totalAgentTimeWarnings = [0 for agent in agents]
self.agentTimeout = False
import io
self.agentOutput = [io.StringIO() for agent in agents]
def getProgress(self):
if self.gameOver:
return 1.0
else:
return self.rules.getProgress(self)
def _agentCrash( self, agentIndex, quiet=False):
"Helper method for handling agent crashes"
if not quiet: traceback.print_exc()
self.gameOver = True
self.agentCrashed = True
self.rules.agentCrash(self, agentIndex)
OLD_STDOUT = None
OLD_STDERR = None
def mute(self, agentIndex):
if not self.muteAgents: return
global OLD_STDOUT, OLD_STDERR
import io
OLD_STDOUT = sys.stdout
OLD_STDERR = sys.stderr
sys.stdout = self.agentOutput[agentIndex]
sys.stderr = self.agentOutput[agentIndex]
def unmute(self):
if not self.muteAgents: return
global OLD_STDOUT, OLD_STDERR
# Revert stdout/stderr to originals
sys.stdout = OLD_STDOUT
sys.stderr = OLD_STDERR
def run( self ):
"""
Main control loop for game play.
"""
self.display.initialize(self.state.data)
self.numMoves = 0
###self.display.initialize(self.state.makeObservation(1).data)
# inform learning agents of the game start
for i in range(len(self.agents)):
agent = self.agents[i]
if not agent:
self.mute(i)
# this is a null agent, meaning it failed to load
# the other team wins
print("Agent %d failed to load" % i, file=sys.stderr)
self.unmute()
self._agentCrash(i, quiet=True)
return
if ("registerInitialState" in dir(agent)):
self.mute(i)
if self.catchExceptions:
try:
timed_func = TimeoutFunction(agent.registerInitialState, int(self.rules.getMaxStartupTime(i)))
try:
start_time = time.time()
timed_func(self.state.deepCopy())
time_taken = time.time() - start_time
self.totalAgentTimes[i] += time_taken
except TimeoutFunctionException:
print("Agent %d ran out of time on startup!" % i, file=sys.stderr)
self.unmute()
self.agentTimeout = True
self._agentCrash(i, quiet=True)
return
except Exception as data:
self._agentCrash(i, quiet=False)
self.unmute()
return
else:
agent.registerInitialState(self.state.deepCopy())
## TODO: could this exceed the total time
self.unmute()
agentIndex = self.startingIndex
numAgents = len( self.agents )
while not self.gameOver:
# Fetch the next agent
agent = self.agents[agentIndex]
move_time = 0
skip_action = False
# Generate an observation of the state
if 'observationFunction' in dir( agent ):
self.mute(agentIndex)
if self.catchExceptions:
try:
timed_func = TimeoutFunction(agent.observationFunction, int(self.rules.getMoveTimeout(agentIndex)))
try:
start_time = time.time()
observation = timed_func(self.state.deepCopy())
except TimeoutFunctionException:
skip_action = True
move_time += time.time() - start_time
self.unmute()
except Exception as data:
self._agentCrash(agentIndex, quiet=False)
self.unmute()
return
else:
observation = agent.observationFunction(self.state.deepCopy())
self.unmute()
else:
observation = self.state.deepCopy()
# Solicit an action
action = None
self.mute(agentIndex)
if self.catchExceptions:
try:
timed_func = TimeoutFunction(agent.getAction, int(self.rules.getMoveTimeout(agentIndex)) - int(move_time))
try:
start_time = time.time()
if skip_action:
raise TimeoutFunctionException()
action = timed_func( observation )
except TimeoutFunctionException:
print("Agent %d timed out on a single move!" % agentIndex, file=sys.stderr)
self.agentTimeout = True
self._agentCrash(agentIndex, quiet=True)
self.unmute()
return
move_time += time.time() - start_time
if move_time > self.rules.getMoveWarningTime(agentIndex):
self.totalAgentTimeWarnings[agentIndex] += 1
print("Agent %d took too long to make a move! This is warning %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex]), file=sys.stderr)
if self.totalAgentTimeWarnings[agentIndex] > self.rules.getMaxTimeWarnings(agentIndex):
print("Agent %d exceeded the maximum number of warnings: %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex]), file=sys.stderr)
self.agentTimeout = True
self._agentCrash(agentIndex, quiet=True)
self.unmute()
return
self.totalAgentTimes[agentIndex] += move_time
#print("Agent: %d, time: %f, total: %f" % (agentIndex, move_time, self.totalAgentTimes[agentIndex]))
if self.totalAgentTimes[agentIndex] > self.rules.getMaxTotalTime(agentIndex):
print("Agent %d ran out of time! (time: %1.2f)" % (agentIndex, self.totalAgentTimes[agentIndex]), file=sys.stderr)
self.agentTimeout = True
self._agentCrash(agentIndex, quiet=True)
self.unmute()
return
self.unmute()
except Exception as data:
self._agentCrash(agentIndex)
self.unmute()
return
else:
action = agent.getAction(observation)
self.unmute()
# Execute the action
self.moveHistory.append( (agentIndex, action) )
if self.catchExceptions:
try:
self.state = self.state.generateSuccessor( agentIndex, action )
except Exception as data:
self.mute(agentIndex)
self._agentCrash(agentIndex)
self.unmute()
return
else:
self.state = self.state.generateSuccessor( agentIndex, action )
# Change the display
self.display.update( self.state.data )
###idx = agentIndex - agentIndex % 2 + 1
###self.display.update( self.state.makeObservation(idx).data )
# Allow for game specific conditions (winning, losing, etc.)
self.rules.process(self.state, self)
# Track progress
if agentIndex == numAgents + 1: self.numMoves += 1
# Next agent
agentIndex = ( agentIndex + 1 ) % numAgents
if _BOINC_ENABLED:
boinc.set_fraction_done(self.getProgress())
# inform a learning agent of the game result
for agentIndex, agent in enumerate(self.agents):
if "final" in dir( agent ) :
try:
self.mute(agentIndex)
agent.final( self.state )
self.unmute()
except Exception as data:
if not self.catchExceptions: raise data
self._agentCrash(agentIndex)
self.unmute()
return
self.display.finish()
+81
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# ghostAgents.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).
from game import Agent
from game import Actions
from game import Directions
import random
from util import manhattanDistance
import util
class GhostAgent( Agent ):
def __init__( self, index ):
self.index = index
def getAction( self, state ):
dist = self.getDistribution(state)
if len(dist) == 0:
return Directions.STOP
else:
return util.chooseFromDistribution( dist )
def getDistribution(self, state):
"Returns a Counter encoding a distribution over actions from the provided state."
util.raiseNotDefined()
class RandomGhost( GhostAgent ):
"A ghost that chooses a legal action uniformly at random."
def getDistribution( self, state ):
dist = util.Counter()
for a in state.getLegalActions( self.index ): dist[a] = 1.0
dist.normalize()
return dist
class DirectionalGhost( GhostAgent ):
"A ghost that prefers to rush Pacman, or flee when scared."
def __init__( self, index, prob_attack=0.8, prob_scaredFlee=0.8 ):
self.index = index
self.prob_attack = prob_attack
self.prob_scaredFlee = prob_scaredFlee
def getDistribution( self, state ):
# Read variables from state
ghostState = state.getGhostState( self.index )
legalActions = state.getLegalActions( self.index )
pos = state.getGhostPosition( self.index )
isScared = ghostState.scaredTimer > 0
speed = 1
if isScared: speed = 0.5
actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
pacmanPosition = state.getPacmanPosition()
# Select best actions given the state
distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
if isScared:
bestScore = max( distancesToPacman )
bestProb = self.prob_scaredFlee
else:
bestScore = min( distancesToPacman )
bestProb = self.prob_attack
bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]
# Construct distribution
dist = util.Counter()
for a in bestActions: dist[a] = bestProb / len(bestActions)
for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
dist.normalize()
return dist
+320
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# grading.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).
"Common code for autograders"
from html import escape
import time
import json
import traceback
from collections import defaultdict
import util
class Grades:
"A data structure for project grades, along with formatting code to display them"
def __init__(self, projectName, questionsAndMaxesList,
gsOutput=False, edxOutput=False, muteOutput=False):
"""
Defines the grading scheme for a project
projectName: project name
questionsAndMaxesDict: a list of (question name, max points per question)
"""
self.questions = [el[0] for el in questionsAndMaxesList]
self.maxes = dict(questionsAndMaxesList)
self.points = Counter()
self.messages = dict([(q, []) for q in self.questions])
self.project = projectName
self.start = time.localtime()[1:6]
self.sane = True # Sanity checks
self.currentQuestion = None # Which question we're grading
self.edxOutput = edxOutput
self.gsOutput = gsOutput # GradeScope output
self.mute = muteOutput
self.prereqs = defaultdict(set)
#print('Autograder transcript for %s' % self.project)
print('Starting on %d-%d at %d:%02d:%02d' % self.start)
def addPrereq(self, question, prereq):
self.prereqs[question].add(prereq)
def grade(self, gradingModule, exceptionMap = {}, bonusPic = False):
"""
Grades each question
gradingModule: the module with all the grading functions (pass in with sys.modules[__name__])
"""
completedQuestions = set([])
for q in self.questions:
print('\nQuestion %s' % q)
print('=' * (9 + len(q)))
print
self.currentQuestion = q
incompleted = self.prereqs[q].difference(completedQuestions)
if len(incompleted) > 0:
prereq = incompleted.pop()
print(
"""*** NOTE: Make sure to complete Question %s before working on Question %s,
*** because Question %s builds upon your answer for Question %s.
""" % (prereq, q, q, prereq))
continue
if self.mute: util.mutePrint()
try:
util.TimeoutFunction(getattr(gradingModule, q),1800)(self) # Call the question's function
#TimeoutFunction(getattr(gradingModule, q),1200)(self) # Call the question's function
except Exception as inst:
self.addExceptionMessage(q, inst, traceback)
self.addErrorHints(exceptionMap, inst, q[1])
except:
self.fail('FAIL: Terminated with a string exception.')
finally:
if self.mute: util.unmutePrint()
if self.points[q] >= self.maxes[q]:
completedQuestions.add(q)
print('\n### Question %s: %d/%d ###\n' % (q, self.points[q], self.maxes[q]))
print('\nFinished at %d:%02d:%02d' % time.localtime()[3:6])
print("\nProvisional grades\n==================")
for q in self.questions:
print('Question %s: %d/%d' % (q, self.points[q], self.maxes[q]))
print('------------------')
print('Total: %d/%d' % (self.points.totalCount(), sum(self.maxes.values())))
if bonusPic and self.points.totalCount() == 25:
print("""
ALL HAIL GRANDPAC.
LONG LIVE THE GHOSTBUSTING KING.
--- ---- ---
| \ / + \ / |
| + \--/ \--/ + |
| + + |
| + + + |
@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
\ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
\ / @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
V \ @@@@@@@@@@@@@@@@@@@@@@@@@@@@
\ / @@@@@@@@@@@@@@@@@@@@@@@@@@
V @@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@
/\ @@@@@@@@@@@@@@@@@@@@@@
/ \ @@@@@@@@@@@@@@@@@@@@@@@@@
/\ / @@@@@@@@@@@@@@@@@@@@@@@@@@@
/ \ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
/ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@
""")
print("""
Your grades are NOT yet registered. To register your grades, make sure
to follow your instructor's guidelines to receive credit on your project.
""")
if self.edxOutput:
self.produceOutput()
if self.gsOutput:
self.produceGradeScopeOutput()
def addExceptionMessage(self, q, inst, traceback):
"""
Method to format the exception message, this is more complicated because
we need to escape the traceback but wrap the exception in a <pre> tag
"""
self.fail('FAIL: Exception raised: %s' % inst)
self.addMessage('')
for line in traceback.format_exc().split('\n'):
self.addMessage(line)
def addErrorHints(self, exceptionMap, errorInstance, questionNum):
typeOf = str(type(errorInstance))
questionName = 'q' + questionNum
errorHint = ''
# question specific error hints
if exceptionMap.get(questionName):
questionMap = exceptionMap.get(questionName)
if (questionMap.get(typeOf)):
errorHint = questionMap.get(typeOf)
# fall back to general error messages if a question specific
# one does not exist
if (exceptionMap.get(typeOf)):
errorHint = exceptionMap.get(typeOf)
# dont include the HTML if we have no error hint
if not errorHint:
return ''
for line in errorHint.split('\n'):
self.addMessage(line)
def produceGradeScopeOutput(self):
out_dct = {}
# total of entire submission
total_possible = sum(self.maxes.values())
total_score = sum(self.points.values())
out_dct['score'] = total_score
out_dct['max_score'] = total_possible
out_dct['output'] = "Total score (%d / %d)" % (total_score, total_possible)
# individual tests
tests_out = []
for name in self.questions:
test_out = {}
# test name
test_out['name'] = name
# test score
test_out['score'] = self.points[name]
test_out['max_score'] = self.maxes[name]
# others
is_correct = self.points[name] >= self.maxes[name]
test_out['output'] = " Question {num} ({points}/{max}) {correct}".format(
num=(name[1] if len(name) == 2 else name),
points=test_out['score'],
max=test_out['max_score'],
correct=('X' if not is_correct else ''),
)
test_out['tags'] = []
tests_out.append(test_out)
out_dct['tests'] = tests_out
# file output
with open('gradescope_response.json', 'w') as outfile:
json.dump(out_dct, outfile)
return
def produceOutput(self):
edxOutput = open('edx_response.html', 'w')
edxOutput.write("<div>")
# first sum
total_possible = sum(self.maxes.values())
total_score = sum(self.points.values())
checkOrX = '<span class="incorrect"/>'
if (total_score >= total_possible):
checkOrX = '<span class="correct"/>'
header = """
<h3>
Total score ({total_score} / {total_possible})
</h3>
""".format(total_score = total_score,
total_possible = total_possible,
checkOrX = checkOrX
)
edxOutput.write(header)
for q in self.questions:
if len(q) == 2:
name = q[1]
else:
name = q
checkOrX = '<span class="incorrect"/>'
if (self.points[q] >= self.maxes[q]):
checkOrX = '<span class="correct"/>'
#messages = '\n<br/>\n'.join(self.messages[q])
messages = "<pre>%s</pre>" % '\n'.join(self.messages[q])
output = """
<div class="test">
<section>
<div class="shortform">
Question {q} ({points}/{max}) {checkOrX}
</div>
<div class="longform">
{messages}
</div>
</section>
</div>
""".format(q = name,
max = self.maxes[q],
messages = messages,
checkOrX = checkOrX,
points = self.points[q]
)
# print("*** output for Question %s " % q[1])
# print(output)
edxOutput.write(output)
edxOutput.write("</div>")
edxOutput.close()
edxOutput = open('edx_grade', 'w')
edxOutput.write(str(self.points.totalCount()))
edxOutput.close()
def fail(self, message, raw=False):
"Sets sanity check bit to false and outputs a message"
self.sane = False
self.assignZeroCredit()
self.addMessage(message, raw)
def assignZeroCredit(self):
self.points[self.currentQuestion] = 0
def addPoints(self, amt):
self.points[self.currentQuestion] += amt
def deductPoints(self, amt):
self.points[self.currentQuestion] -= amt
def assignFullCredit(self, message="", raw=False):
self.points[self.currentQuestion] = self.maxes[self.currentQuestion]
if message != "":
self.addMessage(message, raw)
def addMessage(self, message, raw=False):
if not raw:
# We assume raw messages, formatted for HTML, are printed separately
if self.mute: util.unmutePrint()
print('*** ' + message)
if self.mute: util.mutePrint()
message = escape(message)
self.messages[self.currentQuestion].append(message)
def addMessageToEmail(self, message):
print("WARNING**** addMessageToEmail is deprecated %s" % message)
for line in message.split('\n'):
pass
#print('%%% ' + line + ' %%%')
#self.messages[self.currentQuestion].append(line)
class Counter(dict):
"""
Dict with default 0
"""
def __getitem__(self, idx):
try:
return dict.__getitem__(self, idx)
except KeyError:
return 0
def totalCount(self):
"""
Returns the sum of counts for all keys.
"""
return sum(self.values())
+679
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@@ -0,0 +1,679 @@
# graphicsDisplay.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).
from graphicsUtils import *
import math, time
from game import Directions
###########################
# GRAPHICS DISPLAY CODE #
###########################
# Most code by Dan Klein and John Denero written or rewritten for cs188, UC Berkeley.
# Some code from a Pacman implementation by LiveWires, and used / modified with permission.
DEFAULT_GRID_SIZE = 30.0
INFO_PANE_HEIGHT = 35
BACKGROUND_COLOR = formatColor(0,0,0)
WALL_COLOR = formatColor(0.0/255.0, 51.0/255.0, 255.0/255.0)
INFO_PANE_COLOR = formatColor(.4,.4,0)
SCORE_COLOR = formatColor(.9, .9, .9)
PACMAN_OUTLINE_WIDTH = 2
PACMAN_CAPTURE_OUTLINE_WIDTH = 4
GHOST_COLORS = []
GHOST_COLORS.append(formatColor(.9,0,0)) # Red
GHOST_COLORS.append(formatColor(0,.3,.9)) # Blue
GHOST_COLORS.append(formatColor(.98,.41,.07)) # Orange
GHOST_COLORS.append(formatColor(.1,.75,.7)) # Green
GHOST_COLORS.append(formatColor(1.0,0.6,0.0)) # Yellow
GHOST_COLORS.append(formatColor(.4,0.13,0.91)) # Purple
TEAM_COLORS = GHOST_COLORS[:2]
GHOST_SHAPE = [
( 0, 0.3 ),
( 0.25, 0.75 ),
( 0.5, 0.3 ),
( 0.75, 0.75 ),
( 0.75, -0.5 ),
( 0.5, -0.75 ),
(-0.5, -0.75 ),
(-0.75, -0.5 ),
(-0.75, 0.75 ),
(-0.5, 0.3 ),
(-0.25, 0.75 )
]
GHOST_SIZE = 0.65
SCARED_COLOR = formatColor(1,1,1)
GHOST_VEC_COLORS = [colorToVector(c) for c in GHOST_COLORS]
PACMAN_COLOR = formatColor(255.0/255.0,255.0/255.0,61.0/255)
PACMAN_SCALE = 0.5
#pacman_speed = 0.25
# Food
FOOD_COLOR = formatColor(1,1,1)
FOOD_SIZE = 0.1
# Laser
LASER_COLOR = formatColor(1,0,0)
LASER_SIZE = 0.02
# Capsule graphics
CAPSULE_COLOR = formatColor(1,1,1)
CAPSULE_SIZE = 0.25
# Drawing walls
WALL_RADIUS = 0.15
class InfoPane:
def __init__(self, layout, gridSize):
self.gridSize = gridSize
self.width = (layout.width) * gridSize
self.base = (layout.height + 1) * gridSize
self.height = INFO_PANE_HEIGHT
self.fontSize = 24
self.textColor = PACMAN_COLOR
self.drawPane()
def toScreen(self, pos, y = None):
"""
Translates a point relative from the bottom left of the info pane.
"""
if y == None:
x,y = pos
else:
x = pos
x = self.gridSize + x # Margin
y = self.base + y
return x,y
def drawPane(self):
self.scoreText = text( self.toScreen(0, 0 ), self.textColor, "SCORE: 0", "Times", self.fontSize, "bold")
def initializeGhostDistances(self, distances):
self.ghostDistanceText = []
size = 20
if self.width < 240:
size = 12
if self.width < 160:
size = 10
for i, d in enumerate(distances):
t = text( self.toScreen(self.width//2 + self.width//8 * i, 0), GHOST_COLORS[i+1], d, "Times", size, "bold")
self.ghostDistanceText.append(t)
def updateScore(self, score):
changeText(self.scoreText, "SCORE: % 4d" % score)
def setTeam(self, isBlue):
text = "RED TEAM"
if isBlue: text = "BLUE TEAM"
self.teamText = text( self.toScreen(300, 0 ), self.textColor, text, "Times", self.fontSize, "bold")
def updateGhostDistances(self, distances):
if len(distances) == 0: return
if 'ghostDistanceText' not in dir(self): self.initializeGhostDistances(distances)
else:
for i, d in enumerate(distances):
changeText(self.ghostDistanceText[i], d)
def drawGhost(self):
pass
def drawPacman(self):
pass
def drawWarning(self):
pass
def clearIcon(self):
pass
def updateMessage(self, message):
pass
def clearMessage(self):
pass
class PacmanGraphics:
def __init__(self, zoom=1.0, frameTime=0.0, capture=False):
self.have_window = 0
self.currentGhostImages = {}
self.pacmanImage = None
self.zoom = zoom
self.gridSize = DEFAULT_GRID_SIZE * zoom
self.capture = capture
self.frameTime = frameTime
def checkNullDisplay(self):
return False
def initialize(self, state, isBlue = False):
self.isBlue = isBlue
self.startGraphics(state)
# self.drawDistributions(state)
self.distributionImages = None # Initialized lazily
self.drawStaticObjects(state)
self.drawAgentObjects(state)
# Information
self.previousState = state
def startGraphics(self, state):
self.layout = state.layout
layout = self.layout
self.width = layout.width
self.height = layout.height
self.make_window(self.width, self.height)
self.infoPane = InfoPane(layout, self.gridSize)
self.currentState = layout
def drawDistributions(self, state):
walls = state.layout.walls
dist = []
for x in range(walls.width):
distx = []
dist.append(distx)
for y in range(walls.height):
( screen_x, screen_y ) = self.to_screen( (x, y) )
block = square( (screen_x, screen_y),
0.5 * self.gridSize,
color = BACKGROUND_COLOR,
filled = 1, behind=2)
distx.append(block)
self.distributionImages = dist
def drawStaticObjects(self, state):
layout = self.layout
self.drawWalls(layout.walls)
self.food = self.drawFood(layout.food)
self.capsules = self.drawCapsules(layout.capsules)
refresh()
def drawAgentObjects(self, state):
self.agentImages = [] # (agentState, image)
for index, agent in enumerate(state.agentStates):
if agent.isPacman:
image = self.drawPacman(agent, index)
self.agentImages.append( (agent, image) )
else:
image = self.drawGhost(agent, index)
self.agentImages.append( (agent, image) )
refresh()
def swapImages(self, agentIndex, newState):
"""
Changes an image from a ghost to a pacman or vis versa (for capture)
"""
prevState, prevImage = self.agentImages[agentIndex]
for item in prevImage: remove_from_screen(item)
if newState.isPacman:
image = self.drawPacman(newState, agentIndex)
self.agentImages[agentIndex] = (newState, image )
else:
image = self.drawGhost(newState, agentIndex)
self.agentImages[agentIndex] = (newState, image )
refresh()
def update(self, newState):
agentIndex = newState._agentMoved
agentState = newState.agentStates[agentIndex]
if self.agentImages[agentIndex][0].isPacman != agentState.isPacman: self.swapImages(agentIndex, agentState)
prevState, prevImage = self.agentImages[agentIndex]
if agentState.isPacman:
self.animatePacman(agentState, prevState, prevImage)
else:
self.moveGhost(agentState, agentIndex, prevState, prevImage)
self.agentImages[agentIndex] = (agentState, prevImage)
if newState._foodEaten != None:
self.removeFood(newState._foodEaten, self.food)
if newState._capsuleEaten != None:
self.removeCapsule(newState._capsuleEaten, self.capsules)
self.infoPane.updateScore(newState.score)
if 'ghostDistances' in dir(newState):
self.infoPane.updateGhostDistances(newState.ghostDistances)
def make_window(self, width, height):
grid_width = (width-1) * self.gridSize
grid_height = (height-1) * self.gridSize
screen_width = 2*self.gridSize + grid_width
screen_height = 2*self.gridSize + grid_height + INFO_PANE_HEIGHT
begin_graphics(screen_width,
screen_height,
BACKGROUND_COLOR,
"CS188 Pacman")
def drawPacman(self, pacman, index):
position = self.getPosition(pacman)
screen_point = self.to_screen(position)
endpoints = self.getEndpoints(self.getDirection(pacman))
width = PACMAN_OUTLINE_WIDTH
outlineColor = PACMAN_COLOR
fillColor = PACMAN_COLOR
if self.capture:
outlineColor = TEAM_COLORS[index % 2]
fillColor = GHOST_COLORS[index]
width = PACMAN_CAPTURE_OUTLINE_WIDTH
return [circle(screen_point, PACMAN_SCALE * self.gridSize,
fillColor = fillColor, outlineColor = outlineColor,
endpoints = endpoints,
width = width)]
def getEndpoints(self, direction, position=(0,0)):
x, y = position
pos = x - int(x) + y - int(y)
width = 30 + 80 * math.sin(math.pi* pos)
delta = width / 2
if (direction == 'West'):
endpoints = (180+delta, 180-delta)
elif (direction == 'North'):
endpoints = (90+delta, 90-delta)
elif (direction == 'South'):
endpoints = (270+delta, 270-delta)
else:
endpoints = (0+delta, 0-delta)
return endpoints
def movePacman(self, position, direction, image):
screenPosition = self.to_screen(position)
endpoints = self.getEndpoints( direction, position )
r = PACMAN_SCALE * self.gridSize
moveCircle(image[0], screenPosition, r, endpoints)
refresh()
def animatePacman(self, pacman, prevPacman, image):
if self.frameTime < 0:
print('Press any key to step forward, "q" to play')
keys = wait_for_keys()
if 'q' in keys:
self.frameTime = 0.1
if self.frameTime > 0.01 or self.frameTime < 0:
start = time.time()
fx, fy = self.getPosition(prevPacman)
px, py = self.getPosition(pacman)
frames = 4.0
for i in range(1,int(frames) + 1):
pos = px*i/frames + fx*(frames-i)/frames, py*i/frames + fy*(frames-i)/frames
self.movePacman(pos, self.getDirection(pacman), image)
refresh()
sleep(abs(self.frameTime) / frames)
else:
self.movePacman(self.getPosition(pacman), self.getDirection(pacman), image)
refresh()
def getGhostColor(self, ghost, ghostIndex):
if ghost.scaredTimer > 0:
return SCARED_COLOR
else:
return GHOST_COLORS[ghostIndex]
def drawGhost(self, ghost, agentIndex):
pos = self.getPosition(ghost)
dir = self.getDirection(ghost)
(screen_x, screen_y) = (self.to_screen(pos) )
coords = []
for (x, y) in GHOST_SHAPE:
coords.append((x*self.gridSize*GHOST_SIZE + screen_x, y*self.gridSize*GHOST_SIZE + screen_y))
colour = self.getGhostColor(ghost, agentIndex)
body = polygon(coords, colour, filled = 1)
WHITE = formatColor(1.0, 1.0, 1.0)
BLACK = formatColor(0.0, 0.0, 0.0)
dx = 0
dy = 0
if dir == 'North':
dy = -0.2
if dir == 'South':
dy = 0.2
if dir == 'East':
dx = 0.2
if dir == 'West':
dx = -0.2
leftEye = circle((screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2, WHITE, WHITE)
rightEye = circle((screen_x+self.gridSize*GHOST_SIZE*(0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2, WHITE, WHITE)
leftPupil = circle((screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08, BLACK, BLACK)
rightPupil = circle((screen_x+self.gridSize*GHOST_SIZE*(0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08, BLACK, BLACK)
ghostImageParts = []
ghostImageParts.append(body)
ghostImageParts.append(leftEye)
ghostImageParts.append(rightEye)
ghostImageParts.append(leftPupil)
ghostImageParts.append(rightPupil)
return ghostImageParts
def moveEyes(self, pos, dir, eyes):
(screen_x, screen_y) = (self.to_screen(pos) )
dx = 0
dy = 0
if dir == 'North':
dy = -0.2
if dir == 'South':
dy = 0.2
if dir == 'East':
dx = 0.2
if dir == 'West':
dx = -0.2
moveCircle(eyes[0],(screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2)
moveCircle(eyes[1],(screen_x+self.gridSize*GHOST_SIZE*(0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2)
moveCircle(eyes[2],(screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08)
moveCircle(eyes[3],(screen_x+self.gridSize*GHOST_SIZE*(0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08)
def moveGhost(self, ghost, ghostIndex, prevGhost, ghostImageParts):
old_x, old_y = self.to_screen(self.getPosition(prevGhost))
new_x, new_y = self.to_screen(self.getPosition(ghost))
delta = new_x - old_x, new_y - old_y
for ghostImagePart in ghostImageParts:
move_by(ghostImagePart, delta)
refresh()
if ghost.scaredTimer > 0:
color = SCARED_COLOR
else:
color = GHOST_COLORS[ghostIndex]
edit(ghostImageParts[0], ('fill', color), ('outline', color))
self.moveEyes(self.getPosition(ghost), self.getDirection(ghost), ghostImageParts[-4:])
refresh()
def getPosition(self, agentState):
if agentState.configuration == None: return (-1000, -1000)
return agentState.getPosition()
def getDirection(self, agentState):
if agentState.configuration == None: return Directions.STOP
return agentState.configuration.getDirection()
def finish(self):
end_graphics()
def to_screen(self, point):
( x, y ) = point
#y = self.height - y
x = (x + 1)*self.gridSize
y = (self.height - y)*self.gridSize
return ( x, y )
# Fixes some TK issue with off-center circles
def to_screen2(self, point):
( x, y ) = point
#y = self.height - y
x = (x + 1)*self.gridSize
y = (self.height - y)*self.gridSize
return ( x, y )
def drawWalls(self, wallMatrix):
wallColor = WALL_COLOR
for xNum, x in enumerate(wallMatrix):
if self.capture and (xNum * 2) < wallMatrix.width: wallColor = TEAM_COLORS[0]
if self.capture and (xNum * 2) >= wallMatrix.width: wallColor = TEAM_COLORS[1]
for yNum, cell in enumerate(x):
if cell: # There's a wall here
pos = (xNum, yNum)
screen = self.to_screen(pos)
screen2 = self.to_screen2(pos)
# draw each quadrant of the square based on adjacent walls
wIsWall = self.isWall(xNum-1, yNum, wallMatrix)
eIsWall = self.isWall(xNum+1, yNum, wallMatrix)
nIsWall = self.isWall(xNum, yNum+1, wallMatrix)
sIsWall = self.isWall(xNum, yNum-1, wallMatrix)
nwIsWall = self.isWall(xNum-1, yNum+1, wallMatrix)
swIsWall = self.isWall(xNum-1, yNum-1, wallMatrix)
neIsWall = self.isWall(xNum+1, yNum+1, wallMatrix)
seIsWall = self.isWall(xNum+1, yNum-1, wallMatrix)
# NE quadrant
if (not nIsWall) and (not eIsWall):
# inner circle
circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (0,91), 'arc')
if (nIsWall) and (not eIsWall):
# vertical line
line(add(screen, (self.gridSize*WALL_RADIUS, 0)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-0.5)-1)), wallColor)
if (not nIsWall) and (eIsWall):
# horizontal line
line(add(screen, (0, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5+1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
if (nIsWall) and (eIsWall) and (not neIsWall):
# outer circle
circle(add(screen2, (self.gridSize*2*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (180,271), 'arc')
line(add(screen, (self.gridSize*2*WALL_RADIUS-1, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5+1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
line(add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS+1)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-0.5))), wallColor)
# NW quadrant
if (not nIsWall) and (not wIsWall):
# inner circle
circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (90,181), 'arc')
if (nIsWall) and (not wIsWall):
# vertical line
line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, 0)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-0.5)-1)), wallColor)
if (not nIsWall) and (wIsWall):
# horizontal line
line(add(screen, (0, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5)-1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
if (nIsWall) and (wIsWall) and (not nwIsWall):
# outer circle
circle(add(screen2, (self.gridSize*(-2)*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (270,361), 'arc')
line(add(screen, (self.gridSize*(-2)*WALL_RADIUS+1, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5), self.gridSize*(-1)*WALL_RADIUS)), wallColor)
line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS+1)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-0.5))), wallColor)
# SE quadrant
if (not sIsWall) and (not eIsWall):
# inner circle
circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (270,361), 'arc')
if (sIsWall) and (not eIsWall):
# vertical line
line(add(screen, (self.gridSize*WALL_RADIUS, 0)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(0.5)+1)), wallColor)
if (not sIsWall) and (eIsWall):
# horizontal line
line(add(screen, (0, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5+1, self.gridSize*(1)*WALL_RADIUS)), wallColor)
if (sIsWall) and (eIsWall) and (not seIsWall):
# outer circle
circle(add(screen2, (self.gridSize*2*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (90,181), 'arc')
line(add(screen, (self.gridSize*2*WALL_RADIUS-1, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5, self.gridSize*(1)*WALL_RADIUS)), wallColor)
line(add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS-1)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(0.5))), wallColor)
# SW quadrant
if (not sIsWall) and (not wIsWall):
# inner circle
circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (180,271), 'arc')
if (sIsWall) and (not wIsWall):
# vertical line
line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, 0)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(0.5)+1)), wallColor)
if (not sIsWall) and (wIsWall):
# horizontal line
line(add(screen, (0, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5)-1, self.gridSize*(1)*WALL_RADIUS)), wallColor)
if (sIsWall) and (wIsWall) and (not swIsWall):
# outer circle
circle(add(screen2, (self.gridSize*(-2)*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (0,91), 'arc')
line(add(screen, (self.gridSize*(-2)*WALL_RADIUS+1, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5), self.gridSize*(1)*WALL_RADIUS)), wallColor)
line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS-1)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(0.5))), wallColor)
def isWall(self, x, y, walls):
if x < 0 or y < 0:
return False
if x >= walls.width or y >= walls.height:
return False
return walls[x][y]
def drawFood(self, foodMatrix ):
foodImages = []
color = FOOD_COLOR
for xNum, x in enumerate(foodMatrix):
if self.capture and (xNum * 2) <= foodMatrix.width: color = TEAM_COLORS[0]
if self.capture and (xNum * 2) > foodMatrix.width: color = TEAM_COLORS[1]
imageRow = []
foodImages.append(imageRow)
for yNum, cell in enumerate(x):
if cell: # There's food here
screen = self.to_screen((xNum, yNum ))
dot = circle( screen,
FOOD_SIZE * self.gridSize,
outlineColor = color, fillColor = color,
width = 1)
imageRow.append(dot)
else:
imageRow.append(None)
return foodImages
def drawCapsules(self, capsules ):
capsuleImages = {}
for capsule in capsules:
( screen_x, screen_y ) = self.to_screen(capsule)
dot = circle( (screen_x, screen_y),
CAPSULE_SIZE * self.gridSize,
outlineColor = CAPSULE_COLOR,
fillColor = CAPSULE_COLOR,
width = 1)
capsuleImages[capsule] = dot
return capsuleImages
def removeFood(self, cell, foodImages ):
x, y = cell
remove_from_screen(foodImages[x][y])
def removeCapsule(self, cell, capsuleImages ):
x, y = cell
remove_from_screen(capsuleImages[(x, y)])
def drawExpandedCells(self, cells):
"""
Draws an overlay of expanded grid positions for search agents
"""
n = float(len(cells))
baseColor = [1.0, 0.0, 0.0]
self.clearExpandedCells()
self.expandedCells = []
for k, cell in enumerate(cells):
screenPos = self.to_screen( cell)
cellColor = formatColor(*[(n-k) * c * .5 / n + .25 for c in baseColor])
block = square(screenPos,
0.5 * self.gridSize,
color = cellColor,
filled = 1, behind=2)
self.expandedCells.append(block)
if self.frameTime < 0:
refresh()
def clearExpandedCells(self):
if 'expandedCells' in dir(self) and len(self.expandedCells) > 0:
for cell in self.expandedCells:
remove_from_screen(cell)
def updateDistributions(self, distributions):
"Draws an agent's belief distributions"
# copy all distributions so we don't change their state
distributions = map(lambda x: x.copy(), distributions)
if self.distributionImages == None:
self.drawDistributions(self.previousState)
for x in range(len(self.distributionImages)):
for y in range(len(self.distributionImages[0])):
image = self.distributionImages[x][y]
weights = [dist[ (x,y) ] for dist in distributions]
if sum(weights) != 0:
pass
# Fog of war
color = [0.0,0.0,0.0]
colors = GHOST_VEC_COLORS[1:] # With Pacman
if self.capture: colors = GHOST_VEC_COLORS
for weight, gcolor in zip(weights, colors):
color = [min(1.0, c + 0.95 * g * weight ** .3) for c,g in zip(color, gcolor)]
changeColor(image, formatColor(*color))
refresh()
class FirstPersonPacmanGraphics(PacmanGraphics):
def __init__(self, zoom = 1.0, showGhosts = True, capture = False, frameTime=0):
PacmanGraphics.__init__(self, zoom, frameTime=frameTime)
self.showGhosts = showGhosts
self.capture = capture
def initialize(self, state, isBlue = False):
self.isBlue = isBlue
PacmanGraphics.startGraphics(self, state)
# Initialize distribution images
walls = state.layout.walls
dist = []
self.layout = state.layout
# Draw the rest
self.distributionImages = None # initialize lazily
self.drawStaticObjects(state)
self.drawAgentObjects(state)
# Information
self.previousState = state
def lookAhead(self, config, state):
if config.getDirection() == 'Stop':
return
else:
pass
# Draw relevant ghosts
allGhosts = state.getGhostStates()
visibleGhosts = state.getVisibleGhosts()
for i, ghost in enumerate(allGhosts):
if ghost in visibleGhosts:
self.drawGhost(ghost, i)
else:
self.currentGhostImages[i] = None
def getGhostColor(self, ghost, ghostIndex):
return GHOST_COLORS[ghostIndex]
def getPosition(self, ghostState):
if not self.showGhosts and not ghostState.isPacman and ghostState.getPosition()[1] > 1:
return (-1000, -1000)
else:
return PacmanGraphics.getPosition(self, ghostState)
def add(x, y):
return (x[0] + y[0], x[1] + y[1])
# Saving graphical output
# -----------------------
# Note: to make an animated gif from this postscript output, try the command:
# convert -delay 7 -loop 1 -compress lzw -layers optimize frame* out.gif
# convert is part of imagemagick (freeware)
SAVE_POSTSCRIPT = False
POSTSCRIPT_OUTPUT_DIR = 'frames'
FRAME_NUMBER = 0
import os
def saveFrame():
"Saves the current graphical output as a postscript file"
global SAVE_POSTSCRIPT, FRAME_NUMBER, POSTSCRIPT_OUTPUT_DIR
if not SAVE_POSTSCRIPT: return
if not os.path.exists(POSTSCRIPT_OUTPUT_DIR): os.mkdir(POSTSCRIPT_OUTPUT_DIR)
name = os.path.join(POSTSCRIPT_OUTPUT_DIR, 'frame_%08d.ps' % FRAME_NUMBER)
FRAME_NUMBER += 1
writePostscript(name) # writes the current canvas
+402
View File
@@ -0,0 +1,402 @@
# graphicsUtils.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).
import sys
import math
import random
import string
import time
import types
import tkinter
import os.path
_Windows = sys.platform == 'win32' # True if on Win95/98/NT
_root_window = None # The root window for graphics output
_canvas = None # The canvas which holds graphics
_canvas_xs = None # Size of canvas object
_canvas_ys = None
_canvas_x = None # Current position on canvas
_canvas_y = None
_canvas_col = None # Current colour (set to black below)
_canvas_tsize = 12
_canvas_tserifs = 0
def formatColor(r, g, b):
return '#%02x%02x%02x' % (int(r * 255), int(g * 255), int(b * 255))
def colorToVector(color):
return list(map(lambda x: int(x, 16) / 256.0, [color[1:3], color[3:5], color[5:7]]))
if _Windows:
_canvas_tfonts = ['times new roman', 'lucida console']
else:
_canvas_tfonts = ['times', 'lucidasans-24']
pass # XXX need defaults here
def sleep(secs):
global _root_window
if _root_window == None:
time.sleep(secs)
else:
_root_window.update_idletasks()
_root_window.after(int(1000 * secs), _root_window.quit)
_root_window.mainloop()
def begin_graphics(width=640, height=480, color=formatColor(0, 0, 0), title=None):
global _root_window, _canvas, _canvas_x, _canvas_y, _canvas_xs, _canvas_ys, _bg_color
# Check for duplicate call
if _root_window is not None:
# Lose the window.
_root_window.destroy()
# Save the canvas size parameters
_canvas_xs, _canvas_ys = width - 1, height - 1
_canvas_x, _canvas_y = 0, _canvas_ys
_bg_color = color
# Create the root window
_root_window = tkinter.Tk()
_root_window.protocol('WM_DELETE_WINDOW', _destroy_window)
_root_window.title(title or 'Graphics Window')
_root_window.resizable(0, 0)
# Create the canvas object
try:
_canvas = tkinter.Canvas(_root_window, width=width, height=height)
_canvas.pack()
draw_background()
_canvas.update()
except:
_root_window = None
raise
# Bind to key-down and key-up events
_root_window.bind( "<KeyPress>", _keypress )
_root_window.bind( "<KeyRelease>", _keyrelease )
_root_window.bind( "<FocusIn>", _clear_keys )
_root_window.bind( "<FocusOut>", _clear_keys )
_root_window.bind( "<Button-1>", _leftclick )
_root_window.bind( "<Button-2>", _rightclick )
_root_window.bind( "<Button-3>", _rightclick )
_root_window.bind( "<Control-Button-1>", _ctrl_leftclick)
_clear_keys()
_leftclick_loc = None
_rightclick_loc = None
_ctrl_leftclick_loc = None
def _leftclick(event):
global _leftclick_loc
_leftclick_loc = (event.x, event.y)
def _rightclick(event):
global _rightclick_loc
_rightclick_loc = (event.x, event.y)
def _ctrl_leftclick(event):
global _ctrl_leftclick_loc
_ctrl_leftclick_loc = (event.x, event.y)
def wait_for_click():
while True:
global _leftclick_loc
global _rightclick_loc
global _ctrl_leftclick_loc
if _leftclick_loc != None:
val = _leftclick_loc
_leftclick_loc = None
return val, 'left'
if _rightclick_loc != None:
val = _rightclick_loc
_rightclick_loc = None
return val, 'right'
if _ctrl_leftclick_loc != None:
val = _ctrl_leftclick_loc
_ctrl_leftclick_loc = None
return val, 'ctrl_left'
sleep(0.05)
def draw_background():
corners = [(0,0), (0, _canvas_ys), (_canvas_xs, _canvas_ys), (_canvas_xs, 0)]
polygon(corners, _bg_color, fillColor=_bg_color, filled=True, smoothed=False)
def _destroy_window(event=None):
sys.exit(0)
# global _root_window
# _root_window.destroy()
# _root_window = None
#print("DESTROY")
def end_graphics():
global _root_window, _canvas, _mouse_enabled
try:
try:
sleep(1)
if _root_window != None:
_root_window.destroy()
except SystemExit as e:
print('Ending graphics raised an exception:', e)
finally:
_root_window = None
_canvas = None
_mouse_enabled = 0
_clear_keys()
def clear_screen(background=None):
global _canvas_x, _canvas_y
_canvas.delete('all')
draw_background()
_canvas_x, _canvas_y = 0, _canvas_ys
def polygon(coords, outlineColor, fillColor=None, filled=1, smoothed=1, behind=0, width=1):
c = []
for coord in coords:
c.append(coord[0])
c.append(coord[1])
if fillColor == None: fillColor = outlineColor
if filled == 0: fillColor = ""
poly = _canvas.create_polygon(c, outline=outlineColor, fill=fillColor, smooth=smoothed, width=width)
if behind > 0:
_canvas.tag_lower(poly, behind) # Higher should be more visible
return poly
def square(pos, r, color, filled=1, behind=0):
x, y = pos
coords = [(x - r, y - r), (x + r, y - r), (x + r, y + r), (x - r, y + r)]
return polygon(coords, color, color, filled, 0, behind=behind)
def circle(pos, r, outlineColor, fillColor=None, endpoints=None, style='pieslice', width=2):
x, y = pos
x0, x1 = x - r - 1, x + r
y0, y1 = y - r - 1, y + r
if endpoints == None:
e = [0, 359]
else:
e = list(endpoints)
while e[0] > e[1]: e[1] = e[1] + 360
return _canvas.create_arc(x0, y0, x1, y1, outline=outlineColor, fill=fillColor or outlineColor,
extent=e[1] - e[0], start=e[0], style=style, width=width)
def image(pos, file="../../blueghost.gif"):
x, y = pos
# img = PhotoImage(file=file)
return _canvas.create_image(x, y, image = tkinter.PhotoImage(file=file), anchor = tkinter.NW)
def refresh():
_canvas.update_idletasks()
def moveCircle(id, pos, r, endpoints=None):
global _canvas_x, _canvas_y
x, y = pos
# x0, x1 = x - r, x + r + 1
# y0, y1 = y - r, y + r + 1
x0, x1 = x - r - 1, x + r
y0, y1 = y - r - 1, y + r
if endpoints == None:
e = [0, 359]
else:
e = list(endpoints)
while e[0] > e[1]: e[1] = e[1] + 360
if os.path.isfile('flag'):
edit(id, ('extent', e[1] - e[0]))
else:
edit(id, ('start', e[0]), ('extent', e[1] - e[0]))
move_to(id, x0, y0)
def edit(id, *args):
_canvas.itemconfigure(id, **dict(args))
def text(pos, color, contents, font='Helvetica', size=12, style='normal', anchor="nw"):
global _canvas_x, _canvas_y
x, y = pos
font = (font, str(size), style)
return _canvas.create_text(x, y, fill=color, text=contents, font=font, anchor=anchor)
def changeText(id, newText, font=None, size=12, style='normal'):
_canvas.itemconfigure(id, text=newText)
if font != None:
_canvas.itemconfigure(id, font=(font, '-%d' % size, style))
def changeColor(id, newColor):
_canvas.itemconfigure(id, fill=newColor)
def line(here, there, color=formatColor(0, 0, 0), width=2):
x0, y0 = here[0], here[1]
x1, y1 = there[0], there[1]
return _canvas.create_line(x0, y0, x1, y1, fill=color, width=width)
##############################################################################
### Keypress handling ########################################################
##############################################################################
# We bind to key-down and key-up events.
_keysdown = {}
_keyswaiting = {}
# This holds an unprocessed key release. We delay key releases by up to
# one call to keys_pressed() to get round a problem with auto repeat.
_got_release = None
def _keypress(event):
global _got_release
#remap_arrows(event)
_keysdown[event.keysym] = 1
_keyswaiting[event.keysym] = 1
# print(event.char, event.keycode)
_got_release = None
def _keyrelease(event):
global _got_release
#remap_arrows(event)
try:
del _keysdown[event.keysym]
except:
pass
_got_release = 1
def remap_arrows(event):
# TURN ARROW PRESSES INTO LETTERS (SHOULD BE IN KEYBOARD AGENT)
if event.char in ['a', 's', 'd', 'w']:
return
if event.keycode in [37, 101]: # LEFT ARROW (win / x)
event.char = 'a'
if event.keycode in [38, 99]: # UP ARROW
event.char = 'w'
if event.keycode in [39, 102]: # RIGHT ARROW
event.char = 'd'
if event.keycode in [40, 104]: # DOWN ARROW
event.char = 's'
def _clear_keys(event=None):
global _keysdown, _got_release, _keyswaiting
_keysdown = {}
_keyswaiting = {}
_got_release = None
def keys_pressed(d_o_e=lambda arg: _root_window.dooneevent(arg),
d_w=tkinter._tkinter.DONT_WAIT):
d_o_e(d_w)
if _got_release:
d_o_e(d_w)
return _keysdown.keys()
def keys_waiting():
global _keyswaiting
keys = _keyswaiting.keys()
_keyswaiting = {}
return keys
# Block for a list of keys...
def wait_for_keys():
keys = []
while keys == []:
keys = keys_pressed()
sleep(0.05)
return keys
def remove_from_screen(x,
d_o_e=lambda arg: _root_window.dooneevent(arg),
d_w=tkinter._tkinter.DONT_WAIT):
_canvas.delete(x)
d_o_e(d_w)
def _adjust_coords(coord_list, x, y):
for i in range(0, len(coord_list), 2):
coord_list[i] = coord_list[i] + x
coord_list[i + 1] = coord_list[i + 1] + y
return coord_list
def move_to(object, x, y=None,
d_o_e=lambda arg: _root_window.dooneevent(arg),
d_w=tkinter._tkinter.DONT_WAIT):
if y is None:
try: x, y = x
except: raise 'incomprehensible coordinates'
horiz = True
newCoords = []
current_x, current_y = _canvas.coords(object)[0:2] # first point
for coord in _canvas.coords(object):
if horiz:
inc = x - current_x
else:
inc = y - current_y
horiz = not horiz
newCoords.append(coord + inc)
_canvas.coords(object, *newCoords)
d_o_e(d_w)
def move_by(object, x, y=None,
d_o_e=lambda arg: _root_window.dooneevent(arg),
d_w=tkinter._tkinter.DONT_WAIT, lift=False):
if y is None:
try: x, y = x
except: raise Exception('incomprehensible coordinates')
horiz = True
newCoords = []
for coord in _canvas.coords(object):
if horiz:
inc = x
else:
inc = y
horiz = not horiz
newCoords.append(coord + inc)
_canvas.coords(object, *newCoords)
d_o_e(d_w)
if lift:
_canvas.tag_raise(object)
def writePostscript(filename):
"Writes the current canvas to a postscript file."
psfile = open(filename, 'w')
psfile.write(_canvas.postscript(pageanchor='sw',
y='0.c',
x='0.c'))
psfile.close()
ghost_shape = [
(0, - 0.5),
(0.25, - 0.75),
(0.5, - 0.5),
(0.75, - 0.75),
(0.75, 0.5),
(0.5, 0.75),
(- 0.5, 0.75),
(- 0.75, 0.5),
(- 0.75, - 0.75),
(- 0.5, - 0.5),
(- 0.25, - 0.75)
]
if __name__ == '__main__':
begin_graphics()
clear_screen()
ghost_shape = [(x * 10 + 20, y * 10 + 20) for x, y in ghost_shape]
g = polygon(ghost_shape, formatColor(1, 1, 1))
move_to(g, (50, 50))
circle((150, 150), 20, formatColor(0.7, 0.3, 0.0), endpoints=[15, - 15])
sleep(2)
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# keyboardAgents.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).
from game import Agent
from game import Directions
import random
class KeyboardAgent(Agent):
"""
An agent controlled by the keyboard.
"""
# NOTE: Arrow keys also work.
WEST_KEY = 'a'
EAST_KEY = 'd'
NORTH_KEY = 'w'
SOUTH_KEY = 's'
STOP_KEY = 'q'
def __init__( self, index = 0 ):
self.lastMove = Directions.STOP
self.index = index
self.keys = []
def getAction( self, state):
from graphicsUtils import keys_waiting
from graphicsUtils import keys_pressed
keys = list(keys_waiting()) + list(keys_pressed())
if keys != []:
self.keys = keys
legal = state.getLegalActions(self.index)
move = self.getMove(legal)
if move == Directions.STOP:
# Try to move in the same direction as before
if self.lastMove in legal:
move = self.lastMove
if (self.STOP_KEY in self.keys) and Directions.STOP in legal: move = Directions.STOP
if move not in legal:
move = random.choice(legal)
self.lastMove = move
return move
def getMove(self, legal):
move = Directions.STOP
if (self.WEST_KEY in self.keys or 'Left' in self.keys) and Directions.WEST in legal: move = Directions.WEST
if (self.EAST_KEY in self.keys or 'Right' in self.keys) and Directions.EAST in legal: move = Directions.EAST
if (self.NORTH_KEY in self.keys or 'Up' in self.keys) and Directions.NORTH in legal: move = Directions.NORTH
if (self.SOUTH_KEY in self.keys or 'Down' in self.keys) and Directions.SOUTH in legal: move = Directions.SOUTH
return move
class KeyboardAgent2(KeyboardAgent):
"""
A second agent controlled by the keyboard.
"""
# NOTE: Arrow keys also work.
WEST_KEY = 'j'
EAST_KEY = "l"
NORTH_KEY = 'i'
SOUTH_KEY = 'k'
STOP_KEY = 'u'
def getMove(self, legal):
move = Directions.STOP
if (self.WEST_KEY in self.keys) and Directions.WEST in legal: move = Directions.WEST
if (self.EAST_KEY in self.keys) and Directions.EAST in legal: move = Directions.EAST
if (self.NORTH_KEY in self.keys) and Directions.NORTH in legal: move = Directions.NORTH
if (self.SOUTH_KEY in self.keys) and Directions.SOUTH in legal: move = Directions.SOUTH
return move
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# layout.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).
from util import manhattanDistance
from game import Grid
import os
import random
from functools import reduce
VISIBILITY_MATRIX_CACHE = {}
class Layout:
"""
A Layout manages the static information about the game board.
"""
def __init__(self, layoutText):
self.width = len(layoutText[0])
self.height= len(layoutText)
self.walls = Grid(self.width, self.height, False)
self.food = Grid(self.width, self.height, False)
self.capsules = []
self.agentPositions = []
self.numGhosts = 0
self.processLayoutText(layoutText)
self.layoutText = layoutText
self.totalFood = len(self.food.asList())
# self.initializeVisibilityMatrix()
def getNumGhosts(self):
return self.numGhosts
def initializeVisibilityMatrix(self):
global VISIBILITY_MATRIX_CACHE
if reduce(str.__add__, self.layoutText) not in VISIBILITY_MATRIX_CACHE:
from game import Directions
vecs = [(-0.5,0), (0.5,0),(0,-0.5),(0,0.5)]
dirs = [Directions.NORTH, Directions.SOUTH, Directions.WEST, Directions.EAST]
vis = Grid(self.width, self.height, {Directions.NORTH:set(), Directions.SOUTH:set(), Directions.EAST:set(), Directions.WEST:set(), Directions.STOP:set()})
for x in range(self.width):
for y in range(self.height):
if self.walls[x][y] == False:
for vec, direction in zip(vecs, dirs):
dx, dy = vec
nextx, nexty = x + dx, y + dy
while (nextx + nexty) != int(nextx) + int(nexty) or not self.walls[int(nextx)][int(nexty)] :
vis[x][y][direction].add((nextx, nexty))
nextx, nexty = x + dx, y + dy
self.visibility = vis
VISIBILITY_MATRIX_CACHE[reduce(str.__add__, self.layoutText)] = vis
else:
self.visibility = VISIBILITY_MATRIX_CACHE[reduce(str.__add__, self.layoutText)]
def isWall(self, pos):
x, col = pos
return self.walls[x][col]
def getRandomLegalPosition(self):
x = random.choice(range(self.width))
y = random.choice(range(self.height))
while self.isWall( (x, y) ):
x = random.choice(range(self.width))
y = random.choice(range(self.height))
return (x,y)
def getRandomCorner(self):
poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
return random.choice(poses)
def getFurthestCorner(self, pacPos):
poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
return pos
def isVisibleFrom(self, ghostPos, pacPos, pacDirection):
row, col = [int(x) for x in pacPos]
return ghostPos in self.visibility[row][col][pacDirection]
def __str__(self):
return "\n".join(self.layoutText)
def deepCopy(self):
return Layout(self.layoutText[:])
def processLayoutText(self, layoutText):
"""
Coordinates are flipped from the input format to the (x,y) convention here
The shape of the maze. Each character
represents a different type of object.
% - Wall
. - Food
o - Capsule
G - Ghost
P - Pacman
Other characters are ignored.
"""
maxY = self.height - 1
for y in range(self.height):
for x in range(self.width):
layoutChar = layoutText[maxY - y][x]
self.processLayoutChar(x, y, layoutChar)
self.agentPositions.sort()
self.agentPositions = [ ( i == 0, pos) for i, pos in self.agentPositions]
def processLayoutChar(self, x, y, layoutChar):
if layoutChar == '%':
self.walls[x][y] = True
elif layoutChar == '.':
self.food[x][y] = True
elif layoutChar == 'o':
self.capsules.append((x, y))
elif layoutChar == 'P':
self.agentPositions.append( (0, (x, y) ) )
elif layoutChar in ['G']:
self.agentPositions.append( (1, (x, y) ) )
self.numGhosts += 1
elif layoutChar in ['1', '2', '3', '4']:
self.agentPositions.append( (int(layoutChar), (x,y)))
self.numGhosts += 1
def getLayout(name, back = 2):
if name.endswith('.lay'):
layout = tryToLoad('layouts/' + name)
if layout == None: layout = tryToLoad(name)
else:
layout = tryToLoad('layouts/' + name + '.lay')
if layout == None: layout = tryToLoad(name + '.lay')
if layout == None and back >= 0:
curdir = os.path.abspath('.')
os.chdir('..')
layout = getLayout(name, back -1)
os.chdir(curdir)
return layout
def tryToLoad(fullname):
if(not os.path.exists(fullname)): return None
f = open(fullname)
try: return Layout([line.strip() for line in f])
finally: f.close()
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%. % %.%
% %%%%% % %%% %%% %%%%%%% % %
% % % % % % % %
%%%%% %%%%% %%% % % % %%% %%%%% % %%%
% % % % % % % % % % % % %
% %%% % % % %%% %%%%% %%% % %%% %%% %
% % % % % % % % %
%%% %%%%%%%%% %%%%%%% %%% %%% % % % %
% % % % % % %
% % %%%%% % %%% % % %%% % %%% %%% % %
% % % % % % % % % % % % % %
% % % %%%%%%% % %%%%%%%%% %%% % %%% %
% % % % % % % % % %
%%% %%% % %%%%% %%%%% %%% %%% %%%%% %
% % % % % % % % %
% % % % % % %%% %%% %%% % % % % % %
% % % % % %% % % % % % % % % %
% % %%%%% % %%% %%% % %%% %%% %%%%%
% % % % % % % % % % %
% %%% % % % %%% %%% %%%%%%%%% % %%%
% % % % % % %
% %%% %%%%%%%%%%%%%%%%%%%%% % % %%% %
% % % %
% % % %%%%% %%% % % % % %%%%%%%%%%%%%
% % % % % % % % % % % %
% % %%% %%% % % % %%%%%%%%% %%% % % %
% % % % % % %P % % % % % %
% %%% %%% %%% % %%% % % %%%%% % %%%%%
% % % % % % % %
%%% % %%%%% %%%%% %%% %%% % %%% % %%%
% % % % % % % % % % % % % % %
% % %%% % % % % %%%%%%%%% % % % % % %
% % % %
% % % %%% %%% %%%%%%% %%% %%% %%% %
%.% % % % % .%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % % % % % % %
% %%%%%%% % %%% % %%% %%% %%%%%%% % %
% % % % % % % %
%%%%% %%%%% %%% % % % %%% %%%%% % %%%
% % % % % % % % % % % % % %
% %%% % % % %%% %%%%% %%% % %%% %%% %
% % % % % % % % %
%%% %%%%%%%%% %%%%%%% %%% %%% % % % %
% % % % % % %
% % %%%%% % %%% % % %%% % %%% %%% % %
% % % % % % % % % % % % % %
% % % %%%%%%% % %%%%%%%%% %%% % %%% %
% % % % % % % % % %
%%% %%% % %%%%% %%%%% %%% %%% %%%%% %
% % % % % % % % % % % %
% % % % % %%% %%% %%% %%% % % % % % %
% % % % % % % % %
%%% %%%%%%% % % %%%%% %%% % %%% %%%%%
% % % % % % % % % %
%%%%% % % %%%%%%%%% %%%%%%%%%%% % %%%
% % % % % % % % %
% %%% %%%%% %%%%%%%%% %%%%% % % %%% %
% % % % % % %
% % % %%%%% %%% % % % % %%%%%%%%%%%%%
% % % % % % % % % % % %
% % %%% %%% % % % %%%%%%%%% %%% % % %
% % % % % % % % % % % % %
% %%% %%% %%%%% %%% % % %%%%% % %%%%%
% % % % % % % % %
%%% % %%%%% %%%%% %%% %%% % %%% % %%%
% % % % % % % % % % % % % % %
% % %%% % % % % %%%%%%%%% % % % % % %
% % % % % %
% % % % %%% %%% %%%%%%% %%% %%% %%% %
%.% % % % % % % % P%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%.%.........%% G % o%%%%.....%
%.%.%%%%%%%.%%%%%% %%%%%%%.%%.%
%............%...%............%
%%%%%...%%%.. ..%.%...%.%%%
%o%%%.%%%%%.%%%%%%%.%%%.%.%%%%%
% ..........Po...%...%. o%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%.....%.................%.....%
%.%%%.%.%%%.%%%%%%%.%%%.%.....%
%.%...%.%......%......%.%.....%
%...%%%.%.%%%%.%.%%%%...%%%...%
%%%.%.%.%.%......%..%.%...%.%%%
%...%.%%%.%.%%% %%%.%.%%%.%...%
%.%%%.......% %.......%%%.%
%...%.%%%%%.%%%%%%%.%.%%%.%...%
%%%.%...%.%....%....%.%...%.%%%
%...%%%.%.%%%%.%.%%%%.%.%%%...%
%.......%......%......%.....%.%
%.....%.%%%.%%%%%%%.%%%.%.%%%.%
%.....%........P....%...%.....%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%
%. . . . . % %
% % %
%. . . . . %G%
% % %
%. . . . . % %
% % %
%. . . . . % %
% P %G%
%. . . . . % %
% % %
%. . . . . % %
% % %
%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%
%G. G ....%
%.% % %%%%%% %.%%.%
%.%o% % o% %.o%.%
%.%%%.% %%% %..%.%
%..... P %..%G%
%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%
%o...%........%...o%
%.%%.%.%%..%%.%.%%.%
%...... G GG%......%
%.%.%%.%% %%%.%%.%.%
%.%....% ooo%.%..%.%
%.%.%%.% %% %.%.%%.%
%o%......P....%....%
%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%
% %
% %
% %
% %
% P %
% %
% %
% %
%. %
%%%%%%%%%%%%%%%%%%%%%
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%%%%%%
%....%
% %%.%
% %%.%
%.P .%
%.%%%%
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%%%%%%%%%%%%%%%%%%%%
%o...%........%....%
%.%%.%.%%%%%%.%.%%.%
%.%..............%.%
%.%.%%.%% %%.%%.%.%
%......%G G%......%
%.%.%%.%%%%%%.%%.%.%
%.%..............%.%
%.%%.%.%%%%%%.%.%%.%
%....%...P....%...o%
%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%. % % % %.%
% % % %%%%%% %%%%%%% % %
% % % % % %
%%%%% %%%%% %%% %% %%%%% % %%%
% % % % % % % % %
% %%% % % % %%%%%%%% %%% %%% %
% % %% % % % %
%%% % %%%%%%% %%%% %%% % % % %
% % %% % % %
% % %%%%% % %%%% % %%% %%% % %
% % % % % % %%% %
%. %P%%%%% % %%% % .%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% P%
% %%%%%%%%%%%%%%%%%%% %%% %%%%%%%% %
% %% % % %%% %%% %% ... %
% %% % % % % %%%% %%%%%%%%% %% %%%%%
% %% % % % % % %% %% %% ... %
% %% % % % % % %%%% %%% %%%%%% %
% % % % % % %% %%%%%%%% ... %
% %% % % %%%%%%%% %% %% %%%%%
% %% % %% %%%%%%%%% %% ... %
% %%%%%% %%%%%%% %% %%%%%% %
%%%%%% % %%%% %% % ... %
% %%%%%% %%%%% % %% %% %%%%%
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% %%%%%% %%%%%%%%%%% %% %% %
%%%%%%%%%% %%%%%% %
%. %%%%%%%%%%%%%%%% ...... %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% P%
% %%%%%%%%%%%%%%%%%%%%%%% %%%%%%%% %
% %% % % %%%%%%% %% %
% %% % % % % %%%% %%%%%%%%% %% %%%%%
% %% % % % % %% %% %
% %% % % % % % %%%% %%% %%%%%% %
% % % % % % %% %%%%%%%% %
% %% % % %%%%%%%% %% %% %%%%%
% %% % %% %%%%%%%%% %% %
% %%%%%% %%%%%%% %% %%%%%% %
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% %%%%%% %%%%% % %% %% %%%%%
% %%%%%% % %%%%% %% %
% %%%%%% %%%%%%%%%%% %% %% %
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%. %%%%%%%%%%%%%%%% %
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%.% ....%% G %%%%%% o%%.%
%.%o%%%%%%%.%%%%%%% %%%%%.%
% %%%.%%%%%.%%%%%%%.%%%.%.%%%.%
% ..........Po...%.........%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% P%
% %%%%%%%%%%%%%%%%%%% %%% %%%%%%%% %
% %% % % %%% %%% %%GG %
% %% % % % % %%%% %%%%%%%%% %% %%%%%
% %% % % % % % %%GG %% %
% %% % % % % % %%%%% %%% %%%%%% %
% %% % % % % %% %%%%%%%%% %
% %% % % %%%%%%%% %% %% %%%%%
% %% % %% %%%%%%%%% %% %
% %%% %% %%%%%%% %% %%%%%% %
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% %%%%%% %% %% %% %% %%%%%
% %%%%%% % %%%%% %% %
% %%%% %%%%% %%%%%% %
%%%%%%%% % %%%%%% %
%. %%%%%%%%%%%%%%%% %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%............%%%%%............%
%%%.%...%%%.........%.%...%.%%%
%...%%%.%.%%%%.%.%%%%%%.%%%...%
%.%.....%......%......%.....%.%
%.%%%.%%%%%.%%%%%%%.%%%.%.%%%%%
%.....%........P....%...%.....%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%
%.P G%
% %.%G%%%
%G %%%
%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%
%...%.........%%...%
%.%.%.%%%%%%%%%%.%.%
%..................%
%%%%%%%%.%.%%%%%%%P%
%%%%%%%%....... %
%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%
%.. P .... .... %
%.. ... ... ... ... %
%.. ... ... ... ... %
%.. .... .... G %
%.. ... ... ... ... %
%.. ... ... ... ... %
%.. .... .... o%
%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% P%
% % %
% % %
% % %
% % %
% % %
% % % %
% % % %
% % % %
% % % %
% % % %
% % % %
% % % %
%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%
% % %
% % %
% % %
% %
% %
% %
%. %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# pacman.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).
"""
Pacman.py holds the logic for the classic pacman game along with the main
code to run a game. This file is divided into three sections:
(i) Your interface to the pacman world:
Pacman is a complex environment. You probably don't want to
read through all of the code we wrote to make the game runs
correctly. This section contains the parts of the code
that you will need to understand in order to complete the
project. There is also some code in game.py that you should
understand.
(ii) The hidden secrets of pacman:
This section contains all of the logic code that the pacman
environment uses to decide who can move where, who dies when
things collide, etc. You shouldn't need to read this section
of code, but you can if you want.
(iii) Framework to start a game:
The final section contains the code for reading the command
you use to set up the game, then starting up a new game, along with
linking in all the external parts (agent functions, graphics).
Check this section out to see all the options available to you.
To play your first game, type 'python pacman.py' from the command line.
The keys are 'a', 's', 'd', and 'w' to move (or arrow keys). Have fun!
"""
from game import GameStateData
from game import Game
from game import Directions
from game import Actions
from util import nearestPoint
from util import manhattanDistance
import util, layout
import sys, types, time, random, os
###################################################
# YOUR INTERFACE TO THE PACMAN WORLD: A GameState #
###################################################
class GameState:
"""
A GameState specifies the full game state, including the food, capsules,
agent configurations and score changes.
GameStates are used by the Game object to capture the actual state of the game and
can be used by agents to reason about the game.
Much of the information in a GameState is stored in a GameStateData object. We
strongly suggest that you access that data via the accessor methods below rather
than referring to the GameStateData object directly.
Note that in classic Pacman, Pacman is always agent 0.
"""
####################################################
# Accessor methods: use these to access state data #
####################################################
# static variable keeps track of which states have had getLegalActions called
explored = set()
def getAndResetExplored():
tmp = GameState.explored.copy()
GameState.explored = set()
return tmp
getAndResetExplored = staticmethod(getAndResetExplored)
def getLegalActions( self, agentIndex=0 ):
"""
Returns the legal actions for the agent specified.
"""
# GameState.explored.add(self)
if self.isWin() or self.isLose(): return []
if agentIndex == 0: # Pacman is moving
return PacmanRules.getLegalActions( self )
else:
return GhostRules.getLegalActions( self, agentIndex )
def generateSuccessor( self, agentIndex, action):
"""
Returns the successor state after the specified agent takes the action.
"""
# Check that successors exist
if self.isWin() or self.isLose(): raise Exception('Can\'t generate a successor of a terminal state.')
# Copy current state
state = GameState(self)
# Let agent's logic deal with its action's effects on the board
if agentIndex == 0: # Pacman is moving
state.data._eaten = [False for i in range(state.getNumAgents())]
PacmanRules.applyAction( state, action )
else: # A ghost is moving
GhostRules.applyAction( state, action, agentIndex )
# Time passes
if agentIndex == 0:
state.data.scoreChange += -TIME_PENALTY # Penalty for waiting around
else:
GhostRules.decrementTimer( state.data.agentStates[agentIndex] )
# Resolve multi-agent effects
GhostRules.checkDeath( state, agentIndex )
# Book keeping
state.data._agentMoved = agentIndex
state.data.score += state.data.scoreChange
GameState.explored.add(self)
GameState.explored.add(state)
return state
def getLegalPacmanActions( self ):
return self.getLegalActions( 0 )
def generatePacmanSuccessor( self, action ):
"""
Generates the successor state after the specified pacman move
"""
return self.generateSuccessor( 0, action )
def getPacmanState( self ):
"""
Returns an AgentState object for pacman (in game.py)
state.pos gives the current position
state.direction gives the travel vector
"""
return self.data.agentStates[0].copy()
def getPacmanPosition( self ):
return self.data.agentStates[0].getPosition()
def getGhostStates( self ):
return self.data.agentStates[1:]
def getGhostState( self, agentIndex ):
if agentIndex == 0 or agentIndex >= self.getNumAgents():
raise Exception("Invalid index passed to getGhostState")
return self.data.agentStates[agentIndex]
def getGhostPosition( self, agentIndex ):
if agentIndex == 0:
raise Exception("Pacman's index passed to getGhostPosition")
return self.data.agentStates[agentIndex].getPosition()
def getGhostPositions(self):
return [s.getPosition() for s in self.getGhostStates()]
def getNumAgents( self ):
return len( self.data.agentStates )
def getScore( self ):
return float(self.data.score)
def getCapsules(self):
"""
Returns a list of positions (x,y) of the remaining capsules.
"""
return self.data.capsules
def getNumFood( self ):
return self.data.food.count()
def getFood(self):
"""
Returns a Grid of boolean food indicator variables.
Grids can be accessed via list notation, so to check
if there is food at (x,y), just call
currentFood = state.getFood()
if currentFood[x][y] == True: ...
"""
return self.data.food
def getWalls(self):
"""
Returns a Grid of boolean wall indicator variables.
Grids can be accessed via list notation, so to check
if there is a wall at (x,y), just call
walls = state.getWalls()
if walls[x][y] == True: ...
"""
return self.data.layout.walls
def hasFood(self, x, y):
return self.data.food[x][y]
def hasWall(self, x, y):
return self.data.layout.walls[x][y]
def isLose( self ):
return self.data._lose
def isWin( self ):
return self.data._win
#############################################
# Helper methods: #
# You shouldn't need to call these directly #
#############################################
def __init__( self, prevState = None ):
"""
Generates a new state by copying information from its predecessor.
"""
if prevState != None: # Initial state
self.data = GameStateData(prevState.data)
else:
self.data = GameStateData()
def deepCopy( self ):
state = GameState( self )
state.data = self.data.deepCopy()
return state
def __eq__( self, other ):
"""
Allows two states to be compared.
"""
return hasattr(other, 'data') and self.data == other.data
def __hash__( self ):
"""
Allows states to be keys of dictionaries.
"""
return hash( self.data )
def __str__( self ):
return str(self.data)
def initialize( self, layout, numGhostAgents=1000 ):
"""
Creates an initial game state from a layout array (see layout.py).
"""
self.data.initialize(layout, numGhostAgents)
############################################################################
# THE HIDDEN SECRETS OF PACMAN #
# #
# You shouldn't need to look through the code in this section of the file. #
############################################################################
SCARED_TIME = 40 # Moves ghosts are scared
COLLISION_TOLERANCE = 0.7 # How close ghosts must be to Pacman to kill
TIME_PENALTY = 1 # Number of points lost each round
class ClassicGameRules:
"""
These game rules manage the control flow of a game, deciding when
and how the game starts and ends.
"""
def __init__(self, timeout=30):
self.timeout = timeout
def newGame( self, layout, pacmanAgent, ghostAgents, display, quiet = False, catchExceptions=False):
agents = [pacmanAgent] + ghostAgents[:layout.getNumGhosts()]
initState = GameState()
initState.initialize( layout, len(ghostAgents) )
game = Game(agents, display, self, catchExceptions=catchExceptions)
game.state = initState
self.initialState = initState.deepCopy()
self.quiet = quiet
return game
def process(self, state, game):
"""
Checks to see whether it is time to end the game.
"""
if state.isWin(): self.win(state, game)
if state.isLose(): self.lose(state, game)
def win( self, state, game ):
if not self.quiet: print("Pacman emerges victorious! Score: %d" % state.data.score)
game.gameOver = True
def lose( self, state, game ):
if not self.quiet: print("Pacman died! Score: %d" % state.data.score)
game.gameOver = True
def getProgress(self, game):
return float(game.state.getNumFood()) / self.initialState.getNumFood()
def agentCrash(self, game, agentIndex):
if agentIndex == 0:
print("Pacman crashed")
else:
print("A ghost crashed")
def getMaxTotalTime(self, agentIndex):
return self.timeout
def getMaxStartupTime(self, agentIndex):
return self.timeout
def getMoveWarningTime(self, agentIndex):
return self.timeout
def getMoveTimeout(self, agentIndex):
return self.timeout
def getMaxTimeWarnings(self, agentIndex):
return 0
class PacmanRules:
"""
These functions govern how pacman interacts with his environment under
the classic game rules.
"""
PACMAN_SPEED=1
def getLegalActions( state ):
"""
Returns a list of possible actions.
"""
return Actions.getPossibleActions( state.getPacmanState().configuration, state.data.layout.walls )
getLegalActions = staticmethod( getLegalActions )
def applyAction( state, action ):
"""
Edits the state to reflect the results of the action.
"""
legal = PacmanRules.getLegalActions( state )
if action not in legal:
raise Exception("Illegal action " + str(action))
pacmanState = state.data.agentStates[0]
# Update Configuration
vector = Actions.directionToVector( action, PacmanRules.PACMAN_SPEED )
pacmanState.configuration = pacmanState.configuration.generateSuccessor( vector )
# Eat
next = pacmanState.configuration.getPosition()
nearest = nearestPoint( next )
if manhattanDistance( nearest, next ) <= 0.5 :
# Remove food
PacmanRules.consume( nearest, state )
applyAction = staticmethod( applyAction )
def consume( position, state ):
x,y = position
# Eat food
if state.data.food[x][y]:
state.data.scoreChange += 10
state.data.food = state.data.food.copy()
state.data.food[x][y] = False
state.data._foodEaten = position
# TODO: cache numFood?
numFood = state.getNumFood()
if numFood == 0 and not state.data._lose:
state.data.scoreChange += 500
state.data._win = True
# Eat capsule
if( position in state.getCapsules() ):
state.data.capsules.remove( position )
state.data._capsuleEaten = position
# Reset all ghosts' scared timers
for index in range( 1, len( state.data.agentStates ) ):
state.data.agentStates[index].scaredTimer = SCARED_TIME
consume = staticmethod( consume )
class GhostRules:
"""
These functions dictate how ghosts interact with their environment.
"""
GHOST_SPEED=1.0
def getLegalActions( state, ghostIndex ):
"""
Ghosts cannot stop, and cannot turn around unless they
reach a dead end, but can turn 90 degrees at intersections.
"""
conf = state.getGhostState( ghostIndex ).configuration
possibleActions = Actions.getPossibleActions( conf, state.data.layout.walls )
reverse = Actions.reverseDirection( conf.direction )
if Directions.STOP in possibleActions:
possibleActions.remove( Directions.STOP )
if reverse in possibleActions and len( possibleActions ) > 1:
possibleActions.remove( reverse )
return possibleActions
getLegalActions = staticmethod( getLegalActions )
def applyAction( state, action, ghostIndex):
legal = GhostRules.getLegalActions( state, ghostIndex )
if action not in legal:
raise Exception("Illegal ghost action " + str(action))
ghostState = state.data.agentStates[ghostIndex]
speed = GhostRules.GHOST_SPEED
if ghostState.scaredTimer > 0: speed /= 2.0
vector = Actions.directionToVector( action, speed )
ghostState.configuration = ghostState.configuration.generateSuccessor( vector )
applyAction = staticmethod( applyAction )
def decrementTimer( ghostState):
timer = ghostState.scaredTimer
if timer == 1:
ghostState.configuration.pos = nearestPoint( ghostState.configuration.pos )
ghostState.scaredTimer = max( 0, timer - 1 )
decrementTimer = staticmethod( decrementTimer )
def checkDeath( state, agentIndex):
pacmanPosition = state.getPacmanPosition()
if agentIndex == 0: # Pacman just moved; Anyone can kill him
for index in range( 1, len( state.data.agentStates ) ):
ghostState = state.data.agentStates[index]
ghostPosition = ghostState.configuration.getPosition()
if GhostRules.canKill( pacmanPosition, ghostPosition ):
GhostRules.collide( state, ghostState, index )
else:
ghostState = state.data.agentStates[agentIndex]
ghostPosition = ghostState.configuration.getPosition()
if GhostRules.canKill( pacmanPosition, ghostPosition ):
GhostRules.collide( state, ghostState, agentIndex )
checkDeath = staticmethod( checkDeath )
def collide( state, ghostState, agentIndex):
if ghostState.scaredTimer > 0:
state.data.scoreChange += 200
GhostRules.placeGhost(state, ghostState)
ghostState.scaredTimer = 0
# Added for first-person
state.data._eaten[agentIndex] = True
else:
if not state.data._win:
state.data.scoreChange -= 500
state.data._lose = True
collide = staticmethod( collide )
def canKill( pacmanPosition, ghostPosition ):
return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE
canKill = staticmethod( canKill )
def placeGhost(state, ghostState):
ghostState.configuration = ghostState.start
placeGhost = staticmethod( placeGhost )
#############################
# FRAMEWORK TO START A GAME #
#############################
def default(str):
return str + ' [Default: %default]'
def parseAgentArgs(str):
if str == None: return {}
pieces = str.split(',')
opts = {}
for p in pieces:
if '=' in p:
key, val = p.split('=')
else:
key,val = p, 1
opts[key] = val
return opts
def readCommand( argv ):
"""
Processes the command used to run pacman from the command line.
"""
from optparse import OptionParser
usageStr = """
USAGE: python pacman.py <options>
EXAMPLES: (1) python pacman.py
- starts an interactive game
(2) python pacman.py --layout smallClassic --zoom 2
OR python pacman.py -l smallClassic -z 2
- starts an interactive game on a smaller board, zoomed in
"""
parser = OptionParser(usageStr)
parser.add_option('-n', '--numGames', dest='numGames', type='int',
help=default('the number of GAMES to play'), metavar='GAMES', default=1)
parser.add_option('-l', '--layout', dest='layout',
help=default('the LAYOUT_FILE from which to load the map layout'),
metavar='LAYOUT_FILE', default='mediumClassic')
parser.add_option('-p', '--pacman', dest='pacman',
help=default('the agent TYPE in the pacmanAgents module to use'),
metavar='TYPE', default='KeyboardAgent')
parser.add_option('-t', '--textGraphics', action='store_true', dest='textGraphics',
help='Display output as text only', default=False)
parser.add_option('-q', '--quietTextGraphics', action='store_true', dest='quietGraphics',
help='Generate minimal output and no graphics', default=False)
parser.add_option('-g', '--ghosts', dest='ghost',
help=default('the ghost agent TYPE in the ghostAgents module to use'),
metavar = 'TYPE', default='RandomGhost')
parser.add_option('-k', '--numghosts', type='int', dest='numGhosts',
help=default('The maximum number of ghosts to use'), default=4)
parser.add_option('-z', '--zoom', type='float', dest='zoom',
help=default('Zoom the size of the graphics window'), default=1.0)
parser.add_option('-f', '--fixRandomSeed', action='store_true', dest='fixRandomSeed',
help='Fixes the random seed to always play the same game', default=False)
parser.add_option('-r', '--recordActions', action='store_true', dest='record',
help='Writes game histories to a file (named by the time they were played)', default=False)
parser.add_option('--replay', dest='gameToReplay',
help='A recorded game file (pickle) to replay', default=None)
parser.add_option('-a','--agentArgs',dest='agentArgs',
help='Comma separated values sent to agent. e.g. "opt1=val1,opt2,opt3=val3"')
parser.add_option('-x', '--numTraining', dest='numTraining', type='int',
help=default('How many episodes are training (suppresses output)'), default=0)
parser.add_option('--frameTime', dest='frameTime', type='float',
help=default('Time to delay between frames; <0 means keyboard'), default=0.1)
parser.add_option('-c', '--catchExceptions', action='store_true', dest='catchExceptions',
help='Turns on exception handling and timeouts during games', default=False)
parser.add_option('--timeout', dest='timeout', type='int',
help=default('Maximum length of time an agent can spend computing in a single game'), default=30)
options, otherjunk = parser.parse_args(argv)
if len(otherjunk) != 0:
raise Exception('Command line input not understood: ' + str(otherjunk))
args = dict()
# Fix the random seed
if options.fixRandomSeed: random.seed('cs188')
# Choose a layout
args['layout'] = layout.getLayout( options.layout )
if args['layout'] == None: raise Exception("The layout " + options.layout + " cannot be found")
# Choose a Pacman agent
noKeyboard = options.gameToReplay == None and (options.textGraphics or options.quietGraphics)
pacmanType = loadAgent(options.pacman, noKeyboard)
agentOpts = parseAgentArgs(options.agentArgs)
if options.numTraining > 0:
args['numTraining'] = options.numTraining
if 'numTraining' not in agentOpts: agentOpts['numTraining'] = options.numTraining
pacman = pacmanType(**agentOpts) # Instantiate Pacman with agentArgs
args['pacman'] = pacman
# Don't display training games
if 'numTrain' in agentOpts:
options.numQuiet = int(agentOpts['numTrain'])
options.numIgnore = int(agentOpts['numTrain'])
# Choose a ghost agent
ghostType = loadAgent(options.ghost, noKeyboard)
args['ghosts'] = [ghostType( i+1 ) for i in range( options.numGhosts )]
# Choose a display format
if options.quietGraphics:
import textDisplay
args['display'] = textDisplay.NullGraphics()
elif options.textGraphics:
import textDisplay
textDisplay.SLEEP_TIME = options.frameTime
args['display'] = textDisplay.PacmanGraphics()
else:
import graphicsDisplay
args['display'] = graphicsDisplay.PacmanGraphics(options.zoom, frameTime = options.frameTime)
args['numGames'] = options.numGames
args['record'] = options.record
args['catchExceptions'] = options.catchExceptions
args['timeout'] = options.timeout
# Special case: recorded games don't use the runGames method or args structure
if options.gameToReplay != None:
print('Replaying recorded game %s.' % options.gameToReplay)
import pickle
f = open(options.gameToReplay, 'rb')
try: recorded = pickle.load(f)
finally: f.close()
recorded['display'] = args['display']
replayGame(**recorded)
sys.exit(0)
return args
def loadAgent(pacman, nographics):
# Looks through all pythonPath Directories for the right module,
pythonPathStr = os.path.expandvars("$PYTHONPATH")
if pythonPathStr.find(';') == -1:
pythonPathDirs = pythonPathStr.split(':')
else:
pythonPathDirs = pythonPathStr.split(';')
pythonPathDirs.append('.')
for moduleDir in pythonPathDirs:
if not os.path.isdir(moduleDir): continue
moduleNames = [f for f in os.listdir(moduleDir) if f.endswith('gents.py')]
for modulename in moduleNames:
try:
module = __import__(modulename[:-3])
except ImportError:
continue
if pacman in dir(module):
if nographics and modulename == 'keyboardAgents.py':
raise Exception('Using the keyboard requires graphics (not text display)')
return getattr(module, pacman)
raise Exception('The agent ' + pacman + ' is not specified in any *Agents.py.')
def replayGame( layout, actions, display ):
import pacmanAgents, ghostAgents
rules = ClassicGameRules()
agents = [pacmanAgents.GreedyAgent()] + [ghostAgents.RandomGhost(i+1) for i in range(layout.getNumGhosts())]
game = rules.newGame( layout, agents[0], agents[1:], display )
state = game.state
display.initialize(state.data)
for action in actions:
# Execute the action
state = state.generateSuccessor( *action )
# Change the display
display.update( state.data )
# Allow for game specific conditions (winning, losing, etc.)
rules.process(state, game)
display.finish()
def runGames( layout, pacman, ghosts, display, numGames, record, numTraining = 0, catchExceptions=False, timeout=30 ):
import __main__
__main__.__dict__['_display'] = display
rules = ClassicGameRules(timeout)
games = []
for i in range( numGames ):
beQuiet = i < numTraining
if beQuiet:
# Suppress output and graphics
import textDisplay
gameDisplay = textDisplay.NullGraphics()
rules.quiet = True
else:
gameDisplay = display
rules.quiet = False
game = rules.newGame( layout, pacman, ghosts, gameDisplay, beQuiet, catchExceptions)
game.run()
if not beQuiet: games.append(game)
if record:
import time, pickle
fname = ('recorded-game-%d' % (i + 1)) + '-'.join([str(t) for t in time.localtime()[1:6]])
f = open(fname, 'wb')
components = {'layout': layout, 'actions': game.moveHistory}
pickle.dump(components, f)
f.close()
if (numGames-numTraining) > 0:
scores = [game.state.getScore() for game in games]
wins = [game.state.isWin() for game in games]
winRate = wins.count(True)/ float(len(wins))
print('Average Score:', sum(scores) / float(len(scores)))
print('Scores: ', ', '.join([str(score) for score in scores]))
print('Win Rate: %d/%d (%.2f)' % (wins.count(True), len(wins), winRate))
print('Record: ', ', '.join([ ['Loss', 'Win'][int(w)] for w in wins]))
return games
if __name__ == '__main__':
"""
The main function called when pacman.py is run
from the command line:
> python pacman.py
See the usage string for more details.
> python pacman.py --help
"""
args = readCommand( sys.argv[1:] ) # Get game components based on input
runGames( **args )
# import cProfile
# cProfile.run("runGames( **args )")
pass
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# pacmanAgents.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).
from pacman import Directions
from game import Agent
import random
import game
import util
class LeftTurnAgent(game.Agent):
"An agent that turns left at every opportunity"
def getAction(self, state):
legal = state.getLegalPacmanActions()
current = state.getPacmanState().configuration.direction
if current == Directions.STOP: current = Directions.NORTH
left = Directions.LEFT[current]
if left in legal: return left
if current in legal: return current
if Directions.RIGHT[current] in legal: return Directions.RIGHT[current]
if Directions.LEFT[left] in legal: return Directions.LEFT[left]
return Directions.STOP
class GreedyAgent(Agent):
def __init__(self, evalFn="scoreEvaluation"):
self.evaluationFunction = util.lookup(evalFn, globals())
assert self.evaluationFunction != None
def getAction(self, state):
# Generate candidate actions
legal = state.getLegalPacmanActions()
if Directions.STOP in legal: legal.remove(Directions.STOP)
successors = [(state.generateSuccessor(0, action), action) for action in legal]
scored = [(self.evaluationFunction(state), action) for state, action in successors]
bestScore = max(scored)[0]
bestActions = [pair[1] for pair in scored if pair[0] == bestScore]
return random.choice(bestActions)
def scoreEvaluation(state):
return state.getScore()
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# projectParams.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).
STUDENT_CODE_DEFAULT = 'searchAgents.py,search.py'
PROJECT_TEST_CLASSES = 'searchTestClasses.py'
PROJECT_NAME = 'Project 1: Search'
BONUS_PIC = False
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# 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
from game import Directions
from typing import List
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: SearchProblem) -> List[Directions]:
"""
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.
"""
s = Directions.SOUTH
w = Directions.WEST
return [s, s, w, s, w, w, s, w]
def depthFirstSearch(problem: SearchProblem) -> List[Directions]:
"""
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.
To get started, you might want to try some of these simple commands to
understand the search problem that is being passed in:
print("Start:", problem.getStartState())
print("Is the start a goal?", problem.isGoalState(problem.getStartState()))
print("Start's successors:", problem.getSuccessors(problem.getStartState()))
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
def breadthFirstSearch(problem: SearchProblem) -> List[Directions]:
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
def uniformCostSearch(problem: SearchProblem) -> List[Directions]:
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
def nullHeuristic(state, problem=None) -> float:
"""
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: SearchProblem, heuristic=nullHeuristic) -> List[Directions]:
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
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# 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 typing import List, Tuple, Any
from game import Directions
from game import Agent
from game import Actions
import util
import time
import search
import pacman
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.__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
if self.actions == None:
self.actions = []
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: pacman.GameState):
"""
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
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: Any):
"""
Returns whether this search state is a goal state of the problem.
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
def getSuccessors(self, state: Any):
"""
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: Any, problem: CornersProblem):
"""
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.
"""
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: pacman.GameState):
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: Tuple[Tuple, List[List]], problem: FoodSearchProblem):
"""
Your heuristic for the FoodSearchProblem goes here.
If using A* ever finds a solution that is worse uniform cost search finds,
your search may have a but our your heuristic is not admissible! On the
other hand, inadmissible 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: pacman.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: Tuple[int, int]):
"""
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: Tuple[int, int], point2: Tuple[int, int], gameState: pacman.GameState) -> int:
"""
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))
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# util.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).
# util.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).
import sys
import inspect
import heapq, random
class FixedRandom:
def __init__(self):
fixedState = (3, (2147483648, 507801126, 683453281, 310439348, 2597246090, \
2209084787, 2267831527, 979920060, 3098657677, 37650879, 807947081, 3974896263, \
881243242, 3100634921, 1334775171, 3965168385, 746264660, 4074750168, 500078808, \
776561771, 702988163, 1636311725, 2559226045, 157578202, 2498342920, 2794591496, \
4130598723, 496985844, 2944563015, 3731321600, 3514814613, 3362575829, 3038768745, \
2206497038, 1108748846, 1317460727, 3134077628, 988312410, 1674063516, 746456451, \
3958482413, 1857117812, 708750586, 1583423339, 3466495450, 1536929345, 1137240525, \
3875025632, 2466137587, 1235845595, 4214575620, 3792516855, 657994358, 1241843248, \
1695651859, 3678946666, 1929922113, 2351044952, 2317810202, 2039319015, 460787996, \
3654096216, 4068721415, 1814163703, 2904112444, 1386111013, 574629867, 2654529343, \
3833135042, 2725328455, 552431551, 4006991378, 1331562057, 3710134542, 303171486, \
1203231078, 2670768975, 54570816, 2679609001, 578983064, 1271454725, 3230871056, \
2496832891, 2944938195, 1608828728, 367886575, 2544708204, 103775539, 1912402393, \
1098482180, 2738577070, 3091646463, 1505274463, 2079416566, 659100352, 839995305, \
1696257633, 274389836, 3973303017, 671127655, 1061109122, 517486945, 1379749962, \
3421383928, 3116950429, 2165882425, 2346928266, 2892678711, 2936066049, 1316407868, \
2873411858, 4279682888, 2744351923, 3290373816, 1014377279, 955200944, 4220990860, \
2386098930, 1772997650, 3757346974, 1621616438, 2877097197, 442116595, 2010480266, \
2867861469, 2955352695, 605335967, 2222936009, 2067554933, 4129906358, 1519608541, \
1195006590, 1942991038, 2736562236, 279162408, 1415982909, 4099901426, 1732201505, \
2934657937, 860563237, 2479235483, 3081651097, 2244720867, 3112631622, 1636991639, \
3860393305, 2312061927, 48780114, 1149090394, 2643246550, 1764050647, 3836789087, \
3474859076, 4237194338, 1735191073, 2150369208, 92164394, 756974036, 2314453957, \
323969533, 4267621035, 283649842, 810004843, 727855536, 1757827251, 3334960421, \
3261035106, 38417393, 2660980472, 1256633965, 2184045390, 811213141, 2857482069, \
2237770878, 3891003138, 2787806886, 2435192790, 2249324662, 3507764896, 995388363, \
856944153, 619213904, 3233967826, 3703465555, 3286531781, 3863193356, 2992340714, \
413696855, 3865185632, 1704163171, 3043634452, 2225424707, 2199018022, 3506117517, \
3311559776, 3374443561, 1207829628, 668793165, 1822020716, 2082656160, 1160606415, \
3034757648, 741703672, 3094328738, 459332691, 2702383376, 1610239915, 4162939394, \
557861574, 3805706338, 3832520705, 1248934879, 3250424034, 892335058, 74323433, \
3209751608, 3213220797, 3444035873, 3743886725, 1783837251, 610968664, 580745246, \
4041979504, 201684874, 2673219253, 1377283008, 3497299167, 2344209394, 2304982920, \
3081403782, 2599256854, 3184475235, 3373055826, 695186388, 2423332338, 222864327, \
1258227992, 3627871647, 3487724980, 4027953808, 3053320360, 533627073, 3026232514, \
2340271949, 867277230, 868513116, 2158535651, 2487822909, 3428235761, 3067196046, \
3435119657, 1908441839, 788668797, 3367703138, 3317763187, 908264443, 2252100381, \
764223334, 4127108988, 384641349, 3377374722, 1263833251, 1958694944, 3847832657, \
1253909612, 1096494446, 555725445, 2277045895, 3340096504, 1383318686, 4234428127, \
1072582179, 94169494, 1064509968, 2681151917, 2681864920, 734708852, 1338914021, \
1270409500, 1789469116, 4191988204, 1716329784, 2213764829, 3712538840, 919910444, \
1318414447, 3383806712, 3054941722, 3378649942, 1205735655, 1268136494, 2214009444, \
2532395133, 3232230447, 230294038, 342599089, 772808141, 4096882234, 3146662953, \
2784264306, 1860954704, 2675279609, 2984212876, 2466966981, 2627986059, 2985545332, \
2578042598, 1458940786, 2944243755, 3959506256, 1509151382, 325761900, 942251521, \
4184289782, 2756231555, 3297811774, 1169708099, 3280524138, 3805245319, 3227360276, \
3199632491, 2235795585, 2865407118, 36763651, 2441503575, 3314890374, 1755526087, \
17915536, 1196948233, 949343045, 3815841867, 489007833, 2654997597, 2834744136, \
417688687, 2843220846, 85621843, 747339336, 2043645709, 3520444394, 1825470818, \
647778910, 275904777, 1249389189, 3640887431, 4200779599, 323384601, 3446088641, \
4049835786, 1718989062, 3563787136, 44099190, 3281263107, 22910812, 1826109246, \
745118154, 3392171319, 1571490704, 354891067, 815955642, 1453450421, 940015623, \
796817754, 1260148619, 3898237757, 176670141, 1870249326, 3317738680, 448918002, \
4059166594, 2003827551, 987091377, 224855998, 3520570137, 789522610, 2604445123, \
454472869, 475688926, 2990723466, 523362238, 3897608102, 806637149, 2642229586, \
2928614432, 1564415411, 1691381054, 3816907227, 4082581003, 1895544448, 3728217394, \
3214813157, 4054301607, 1882632454, 2873728645, 3694943071, 1297991732, 2101682438, \
3952579552, 678650400, 1391722293, 478833748, 2976468591, 158586606, 2576499787, \
662690848, 3799889765, 3328894692, 2474578497, 2383901391, 1718193504, 3003184595, \
3630561213, 1929441113, 3848238627, 1594310094, 3040359840, 3051803867, 2462788790, \
954409915, 802581771, 681703307, 545982392, 2738993819, 8025358, 2827719383, \
770471093, 3484895980, 3111306320, 3900000891, 2116916652, 397746721, 2087689510, \
721433935, 1396088885, 2751612384, 1998988613, 2135074843, 2521131298, 707009172, \
2398321482, 688041159, 2264560137, 482388305, 207864885, 3735036991, 3490348331, \
1963642811, 3260224305, 3493564223, 1939428454, 1128799656, 1366012432, 2858822447, \
1428147157, 2261125391, 1611208390, 1134826333, 2374102525, 3833625209, 2266397263, \
3189115077, 770080230, 2674657172, 4280146640, 3604531615, 4235071805, 3436987249, \
509704467, 2582695198, 4256268040, 3391197562, 1460642842, 1617931012, 457825497, \
1031452907, 1330422862, 4125947620, 2280712485, 431892090, 2387410588, 2061126784, \
896457479, 3480499461, 2488196663, 4021103792, 1877063114, 2744470201, 1046140599, \
2129952955, 3583049218, 4217723693, 2720341743, 820661843, 1079873609, 3360954200, \
3652304997, 3335838575, 2178810636, 1908053374, 4026721976, 1793145418, 476541615, \
973420250, 515553040, 919292001, 2601786155, 1685119450, 3030170809, 1590676150, \
1665099167, 651151584, 2077190587, 957892642, 646336572, 2743719258, 866169074, \
851118829, 4225766285, 963748226, 799549420, 1955032629, 799460000, 2425744063, \
2441291571, 1928963772, 528930629, 2591962884, 3495142819, 1896021824, 901320159, \
3181820243, 843061941, 3338628510, 3782438992, 9515330, 1705797226, 953535929, \
764833876, 3202464965, 2970244591, 519154982, 3390617541, 566616744, 3438031503, \
1853838297, 170608755, 1393728434, 676900116, 3184965776, 1843100290, 78995357, \
2227939888, 3460264600, 1745705055, 1474086965, 572796246, 4081303004, 882828851, \
1295445825, 137639900, 3304579600, 2722437017, 4093422709, 273203373, 2666507854, \
3998836510, 493829981, 1623949669, 3482036755, 3390023939, 833233937, 1639668730, \
1499455075, 249728260, 1210694006, 3836497489, 1551488720, 3253074267, 3388238003, \
2372035079, 3945715164, 2029501215, 3362012634, 2007375355, 4074709820, 631485888, \
3135015769, 4273087084, 3648076204, 2739943601, 1374020358, 1760722448, 3773939706, \
1313027823, 1895251226, 4224465911, 421382535, 1141067370, 3660034846, 3393185650, \
1850995280, 1451917312, 3841455409, 3926840308, 1397397252, 2572864479, 2500171350, \
3119920613, 531400869, 1626487579, 1099320497, 407414753, 2438623324, 99073255, \
3175491512, 656431560, 1153671785, 236307875, 2824738046, 2320621382, 892174056, \
230984053, 719791226, 2718891946, 624), None)
self.random = random.Random()
self.random.setstate(fixedState)
"""
Data structures useful for implementing SearchAgents
"""
class Stack:
"A container with a last-in-first-out (LIFO) queuing policy."
def __init__(self):
self.list = []
def push(self,item):
"Push 'item' onto the stack"
self.list.append(item)
def pop(self):
"Pop the most recently pushed item from the stack"
return self.list.pop()
def isEmpty(self):
"Returns true if the stack is empty"
return len(self.list) == 0
class Queue:
"A container with a first-in-first-out (FIFO) queuing policy."
def __init__(self):
self.list = []
def push(self,item):
"Enqueue the 'item' into the queue"
self.list.insert(0,item)
def pop(self):
"""
Dequeue the earliest enqueued item still in the queue. This
operation removes the item from the queue.
"""
return self.list.pop()
def isEmpty(self):
"Returns true if the queue is empty"
return len(self.list) == 0
class PriorityQueue:
"""
Implements a priority queue data structure. Each inserted item
has a priority associated with it and the client is usually interested
in quick retrieval of the lowest-priority item in the queue. This
data structure allows O(1) access to the lowest-priority item.
"""
def __init__(self):
self.heap = []
self.count = 0
def push(self, item, priority):
entry = (priority, self.count, item)
heapq.heappush(self.heap, entry)
self.count += 1
def pop(self):
(_, _, item) = heapq.heappop(self.heap)
return item
def isEmpty(self):
return len(self.heap) == 0
def update(self, item, priority):
# If item already in priority queue with higher priority, update its priority and rebuild the heap.
# If item already in priority queue with equal or lower priority, do nothing.
# If item not in priority queue, do the same thing as self.push.
for index, (p, c, i) in enumerate(self.heap):
if i == item:
if p <= priority:
break
del self.heap[index]
self.heap.append((priority, c, item))
heapq.heapify(self.heap)
break
else:
self.push(item, priority)
class PriorityQueueWithFunction(PriorityQueue):
"""
Implements a priority queue with the same push/pop signature of the
Queue and the Stack classes. This is designed for drop-in replacement for
those two classes. The caller has to provide a priority function, which
extracts each item's priority.
"""
def __init__(self, priorityFunction):
"priorityFunction (item) -> priority"
self.priorityFunction = priorityFunction # store the priority function
PriorityQueue.__init__(self) # super-class initializer
def push(self, item):
"Adds an item to the queue with priority from the priority function"
PriorityQueue.push(self, item, self.priorityFunction(item))
def manhattanDistance( xy1, xy2 ):
"Returns the Manhattan distance between points xy1 and xy2"
return abs( xy1[0] - xy2[0] ) + abs( xy1[1] - xy2[1] )
"""
Data structures and functions useful for various course projects
The search project should not need anything below this line.
"""
class Counter(dict):
"""
A counter keeps track of counts for a set of keys.
The counter class is an extension of the standard python
dictionary type. It is specialized to have number values
(integers or floats), and includes a handful of additional
functions to ease the task of counting data. In particular,
all keys are defaulted to have value 0. Using a dictionary:
a = {}
print(a['test'])
would give an error, while the Counter class analogue:
>>> a = Counter()
>>> print(a['test'])
0
returns the default 0 value. Note that to reference a key
that you know is contained in the counter,
you can still use the dictionary syntax:
>>> a = Counter()
>>> a['test'] = 2
>>> print(a['test'])
2
This is very useful for counting things without initializing their counts,
see for example:
>>> a['blah'] += 1
>>> print(a['blah'])
1
The counter also includes additional functionality useful in implementing
the classifiers for this assignment. Two counters can be added,
subtracted or multiplied together. See below for details. They can
also be normalized and their total count and arg max can be extracted.
"""
def __getitem__(self, idx):
self.setdefault(idx, 0)
return dict.__getitem__(self, idx)
def incrementAll(self, keys, count):
"""
Increments all elements of keys by the same count.
>>> a = Counter()
>>> a.incrementAll(['one','two', 'three'], 1)
>>> a['one']
1
>>> a['two']
1
"""
for key in keys:
self[key] += count
def argMax(self):
"""
Returns the key with the highest value.
"""
if len(self.keys()) == 0: return None
all = self.items()
values = [x[1] for x in all]
maxIndex = values.index(max(values))
return all[maxIndex][0]
def sortedKeys(self):
"""
Returns a list of keys sorted by their values. Keys
with the highest values will appear first.
>>> a = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> a['third'] = 1
>>> a.sortedKeys()
['second', 'third', 'first']
"""
sortedItems = self.items()
compare = lambda x, y: sign(y[1] - x[1])
sortedItems.sort(cmp=compare)
return [x[0] for x in sortedItems]
def totalCount(self):
"""
Returns the sum of counts for all keys.
"""
return sum(self.values())
def normalize(self):
"""
Edits the counter such that the total count of all
keys sums to 1. The ratio of counts for all keys
will remain the same. Note that normalizing an empty
Counter will result in an error.
"""
total = float(self.totalCount())
if total == 0: return
for key in self.keys():
self[key] = self[key] / total
def divideAll(self, divisor):
"""
Divides all counts by divisor
"""
divisor = float(divisor)
for key in self:
self[key] /= divisor
def copy(self):
"""
Returns a copy of the counter
"""
return Counter(dict.copy(self))
def __mul__(self, y ):
"""
Multiplying two counters gives the dot product of their vectors where
each unique label is a vector element.
>>> a = Counter()
>>> b = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> b['first'] = 3
>>> b['second'] = 5
>>> a['third'] = 1.5
>>> a['fourth'] = 2.5
>>> a * b
14
"""
sum = 0
x = self
if len(x) > len(y):
x,y = y,x
for key in x:
if key not in y:
continue
sum += x[key] * y[key]
return sum
def __radd__(self, y):
"""
Adding another counter to a counter increments the current counter
by the values stored in the second counter.
>>> a = Counter()
>>> b = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> b['first'] = 3
>>> b['third'] = 1
>>> a += b
>>> a['first']
1
"""
for key, value in y.items():
self[key] += value
def __add__( self, y ):
"""
Adding two counters gives a counter with the union of all keys and
counts of the second added to counts of the first.
>>> a = Counter()
>>> b = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> b['first'] = 3
>>> b['third'] = 1
>>> (a + b)['first']
1
"""
addend = Counter()
for key in self:
if key in y:
addend[key] = self[key] + y[key]
else:
addend[key] = self[key]
for key in y:
if key in self:
continue
addend[key] = y[key]
return addend
def __sub__( self, y ):
"""
Subtracting a counter from another gives a counter with the union of all keys and
counts of the second subtracted from counts of the first.
>>> a = Counter()
>>> b = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> b['first'] = 3
>>> b['third'] = 1
>>> (a - b)['first']
-5
"""
addend = Counter()
for key in self:
if key in y:
addend[key] = self[key] - y[key]
else:
addend[key] = self[key]
for key in y:
if key in self:
continue
addend[key] = -1 * y[key]
return addend
def raiseNotDefined():
fileName = inspect.stack()[1][1]
line = inspect.stack()[1][2]
method = inspect.stack()[1][3]
print("*** Method not implemented: %s at line %s of %s" % (method, line, fileName))
sys.exit(1)
def normalize(vectorOrCounter):
"""
normalize a vector or counter by dividing each value by the sum of all values
"""
normalizedCounter = Counter()
if type(vectorOrCounter) == type(normalizedCounter):
counter = vectorOrCounter
total = float(counter.totalCount())
if total == 0: return counter
for key in counter.keys():
value = counter[key]
normalizedCounter[key] = value / total
return normalizedCounter
else:
vector = vectorOrCounter
s = float(sum(vector))
if s == 0: return vector
return [el / s for el in vector]
def nSample(distribution, values, n):
if sum(distribution) != 1:
distribution = normalize(distribution)
rand = [random.random() for i in range(n)]
rand.sort()
samples = []
samplePos, distPos, cdf = 0,0, distribution[0]
while samplePos < n:
if rand[samplePos] < cdf:
samplePos += 1
samples.append(values[distPos])
else:
distPos += 1
cdf += distribution[distPos]
return samples
def sample(distribution, values = None):
if type(distribution) == Counter:
items = sorted(distribution.items())
distribution = [i[1] for i in items]
values = [i[0] for i in items]
if sum(distribution) != 1:
distribution = normalize(distribution)
choice = random.random()
i, total= 0, distribution[0]
while choice > total:
i += 1
total += distribution[i]
return values[i]
def sampleFromCounter(ctr):
items = sorted(ctr.items())
return sample([v for k,v in items], [k for k,v in items])
def getProbability(value, distribution, values):
"""
Gives the probability of a value under a discrete distribution
defined by (distributions, values).
"""
total = 0.0
for prob, val in zip(distribution, values):
if val == value:
total += prob
return total
def flipCoin( p ):
r = random.random()
return r < p
def chooseFromDistribution( distribution ):
"Takes either a counter or a list of (prob, key) pairs and samples"
if type(distribution) == dict or type(distribution) == Counter:
return sample(distribution)
r = random.random()
base = 0.0
for prob, element in distribution:
base += prob
if r <= base: return element
def nearestPoint( pos ):
"""
Finds the nearest grid point to a position (discretizes).
"""
( current_row, current_col ) = pos
grid_row = int( current_row + 0.5 )
grid_col = int( current_col + 0.5 )
return ( grid_row, grid_col )
def sign( x ):
"""
Returns 1 or -1 depending on the sign of x
"""
if( x >= 0 ):
return 1
else:
return -1
def arrayInvert(array):
"""
Inverts a matrix stored as a list of lists.
"""
result = [[] for i in array]
for outer in array:
for inner in range(len(outer)):
result[inner].append(outer[inner])
return result
def matrixAsList( matrix, value = True ):
"""
Turns a matrix into a list of coordinates matching the specified value
"""
rows, cols = len( matrix ), len( matrix[0] )
cells = []
for row in range( rows ):
for col in range( cols ):
if matrix[row][col] == value:
cells.append( ( row, col ) )
return cells
def lookup(name, namespace):
"""
Get a method or class from any imported module from its name.
Usage: lookup(functionName, globals())
"""
dots = name.count('.')
if dots > 0:
moduleName, objName = '.'.join(name.split('.')[:-1]), name.split('.')[-1]
module = __import__(moduleName)
return getattr(module, objName)
else:
modules = [obj for obj in namespace.values() if str(type(obj)) == "<type 'module'>"]
options = [getattr(module, name) for module in modules if name in dir(module)]
options += [obj[1] for obj in namespace.items() if obj[0] == name ]
if len(options) == 1: return options[0]
if len(options) > 1: raise Exception('Name conflict for %s')
raise Exception('%s not found as a method or class' % name)
def pause():
"""
Pauses the output stream awaiting user feedback.
"""
print("<Press enter/return to continue>")
input()
# code to handle timeouts
#
# FIXME
# NOTE: TimeoutFuncton is NOT reentrant. Later timeouts will silently
# disable earlier timeouts. Could be solved by maintaining a global list
# of active time outs. Currently, questions which have test cases calling
# this have all student code so wrapped.
#
import signal
import time
class TimeoutFunctionException(Exception):
"""Exception to raise on a timeout"""
pass
class TimeoutFunction:
def __init__(self, function, timeout):
self.timeout = timeout
self.function = function
def handle_timeout(self, signum, frame):
raise TimeoutFunctionException()
def __call__(self, *args, **keyArgs):
# If we have SIGALRM signal, use it to cause an exception if and
# when this function runs too long. Otherwise check the time taken
# after the method has returned, and throw an exception then.
if hasattr(signal, 'SIGALRM'):
old = signal.signal(signal.SIGALRM, self.handle_timeout)
signal.alarm(self.timeout)
try:
result = self.function(*args, **keyArgs)
finally:
signal.signal(signal.SIGALRM, old)
signal.alarm(0)
else:
startTime = time.time()
result = self.function(*args, **keyArgs)
timeElapsed = time.time() - startTime
if timeElapsed >= self.timeout:
self.handle_timeout(None, None)
return result
_ORIGINAL_STDOUT = None
_ORIGINAL_STDERR = None
_MUTED = False
class WritableNull:
def write(self, string):
pass
def mutePrint():
global _ORIGINAL_STDOUT, _ORIGINAL_STDERR, _MUTED
if _MUTED:
return
_MUTED = True
_ORIGINAL_STDOUT = sys.stdout
#_ORIGINAL_STDERR = sys.stderr
sys.stdout = WritableNull()
#sys.stderr = WritableNull()
def unmutePrint():
global _ORIGINAL_STDOUT, _ORIGINAL_STDERR, _MUTED
if not _MUTED:
return
_MUTED = False
sys.stdout = _ORIGINAL_STDOUT
#sys.stderr = _ORIGINAL_STDERR