# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
# gamestate.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 pacman_utils.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 gamestate.py' from the command line.
The keys are 'a', 's', 'd', and 'w' to move (or arrow keys). Have fun!
"""
import irlc.pacman.pacman_utils
from irlc.pacman.pacman_utils import GameStateData
from irlc.pacman.pacman_utils import Game
from irlc.pacman.pacman_utils import Directions
from irlc.pacman.pacman_utils import Actions
###################################################
# YOUR INTERFACE TO THE PACMAN WORLD: A GameState #
###################################################
[docs]
class GameState:
r"""
A `GameState` specifies the full game state, including the food, capsules,
agent configurations and score changes.
`GameState`\ s 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.
To get you started, here are some examples.
.. runblock:: pycon
>>> from irlc.pacman.pacman_environment import PacmanEnvironment, very_small_haunted_maze
>>> env = PacmanEnvironment(layout_str=very_small_haunted_maze)
>>> state, _ = env.reset() # Get starting state
>>> print(state)
In the above code, `state` is a `GameState` instance -- i.e. has all the methods found in
*this* class. So for instance to know if the game is won or lost you can do:
.. runblock:: pycon
>>> from irlc.pacman.pacman_environment import PacmanEnvironment, very_small_haunted_maze
>>> env = PacmanEnvironment(layout_str=very_small_haunted_maze)
>>> state, _ = env.reset() # Get starting state
>>> print("Did we win?", state.is_won(), "did we loose?", state.is_lost())
Or to get the available actions, and then the *next* state representing what occurs when you take an action `a`:
.. runblock:: pycon
>>> from irlc.pacman.pacman_environment import PacmanEnvironment, very_small_haunted_maze
>>> env = PacmanEnvironment(layout_str=very_small_haunted_maze)
>>> state, _ = env.reset() # Get starting state
>>> actions = state.A()
>>> print("Available actions are", actions)
>>> next_state = state.f(actions[0]) # Take the first action
>>> print(next_state) # Result of taking the first of the available actions.
When a ghost move, it will select randomly between the available actions. Thus, the chance of a single move is :python:`1/len(state.A())`.
"""
#####################################################
# 02465-relevant stuff: These methods allows you to #
# interact with the game-state. See comments above. #
#####################################################
[docs]
def player(self) -> int:
"""Return the current player.
The players take turns. Initially ``player=0``, meaning it is Pacman (your) turn, and in case there are ghosts
player will then increment until all ghosts have moved at which point ``player = 0`` again and the game is ready
for the next step.
:return: The id of the player who will make the next move.
"""
return self._player
[docs]
def players(self):
"""Return the total number of players.
:return: Return the number of ghosts + 1 (pacman).
"""
return self.getNumAgents()
[docs]
def A(self):
"""Return the available actions for the current player in this state.
If the state is won/lost, the actions will be just the stop-action: ``["Stop"]``.
:return: Available actions as a list.
"""
if self.is_won() or self.is_lost():
return [Directions.STOP]
else:
return self.getLegalActions(self.player())
[docs]
def f(self, a : str) -> object:
"""Let the current player take action ``a``.
This will return a new GameState corresponding to the current player taking an action.
:param a: The action to take.
:return: The next GameState.
"""
if self.is_won() or self.is_lost():
return self
suc = self.generateSuccessor(self.player(), a)
suc._player = (self.player() + 1) % self.getNumAgents()
return suc
[docs]
def is_lost(self):
"""Determine if this is a lost game.
:return: ``True`` if this GameState corresponds to a lost game (a ghost ate pacman)
"""
return self.data._lose
[docs]
def is_won(self):
"""Determine if this is a won game.
:return: ``True`` if this GameState corresponds to a won game (all pellets eaten)
"""
return self.data._win
##################################################################################################
# End of 02465-related stuff. These methods are internal to the game and should **not** be used. #
##################################################################################################
# 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.
# """
if self.is_won() or self.is_lost(): 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.is_won() or self.is_lost(): 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 pacman_utils.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]
#############################################
# Helper methods: #
# You shouldn't need to call these directly #
#############################################
[docs]
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()
self._player = 0
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, quiet = False, catchExceptions=False, time_penalty=TIME_PENALTY):
agents = [pacmanAgent] + ghostAgents[:layout.getNumGhosts()]
initState = GameState() # Time penalty is my idea
initState.initialize( layout, len(ghostAgents) )
game = Game(agents=agents, rules=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.is_won(): self.win(state, game)
if state.is_lost(): 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 gamestate.py <options>
# EXAMPLES: (1) python gamestate.py
# - starts an interactive game
# (2) python gamestate.py --layout smallClassic --zoom 2
# OR python gamestate.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 text_display_pacman
# args['display'] = text_display_pacman.NullGraphics()
# elif options.textGraphics:
# import text_display_pacman
# text_display_pacman.SLEEP_TIME = options.frameTime
# args['display'] = text_display_pacman.PacmanGraphics()
# else:
# pass
# # from gympackman import ggraphicsDisplay
# # args['display'] = ggraphicsDisplay.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 cPickle
# f = open(options.gameToReplay)
# try: recorded = cPickle.load(f)
# finally: f.close()
# recorded['display'] = args['display']
# replayGame(**recorded)
# sys.exit(0)
#
# args['options'] = options
# 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('.')
# from irlc.berkley import pacman as pcman
# pythonPathDirs.append(os.path.dirname(pcman.__file__))
# if pacman == 'PacmanQAgent':
# from irlc.berkley.pacman.qlearningAgents import QLearningAgent
# return QLearningAgent
# if pacman == 'RandomGhost':
# from irlc.berkley.pacman.ghostAgents import RandomGhost
# return RandomGhost
#
# for moduleDir in pythonPathDirs:
# if not os.path.isdir(moduleDir): continue
# moduleNames = [f for f in os.listdir(moduleDir) if f.endswith('gents.py')]
# print(moduleNames)
# for modulename in moduleNames:
# try:
# module = __import__(modulename[:-3])
# except ImportError:
# continue
# print(module)
# 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 ghostAgents
# from irlc.berkley import pacmanAgents
# rules = ClassicGameRules()
# agents = [pacmanAgents.GreedyAgent()] + [irlc.pacman.pacman_utils.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__
# global __main__
# __main__.__dict__['_display'] = display
rules = ClassicGameRules(timeout)
games = []
for i in range( numGames ):
beQuiet = i < numTraining
if beQuiet:
# Suppress output and graphics
import text_display_pacman
gameDisplay = text_display_pacman.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, cPickle
fname = ('recorded-game-%d' % (i + 1)) + '-'.join([str(t) for t in time.localtime()[1:6]])
with open(fname, "w") as f:
# f = file(fname, 'w')
components = {'layout': layout, 'actions': game.moveHistory}
cPickle.dump(components, f)
# f.close()
if (numGames-numTraining) > 0:
scores = [game.state.getScore() for game in games]
wins = [game.state.is_won() 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 gamestate.py is run
# from the command line:
#
# > python gamestate.py
#
# See the usage string for more details.
#
# > python gamestate.py --help
# """
# import sys
#
# sys.adaptor = 'tk'
# # sys.adaptor = 'gym'
# ss = "-p PacmanQAgent -n 1 -l mediumGrid -a numTraining=100"
#
# sys.argv.extend(ss.split())
# args = readCommand( sys.argv[1:] ) # Get game components based on input
# runGames( **args )
#
# # import cProfile
# # cProfile.run("runGames( **args )")
# pass
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 manhattanDistance( xy1, xy2 ):
"Returns the Manhattan distance between points xy1 and xy2"
return abs( xy1[0] - xy2[0] ) + abs( xy1[1] - xy2[1] )
