def run( self ):
"""
Main control loop for game play.
"""
self.initializeGhostPowers()
self.initializeAgentObservedVars()
self.initializePacmanPowers()
self.display.initialize(self.state.data)
self.numMoves = 0
###self.display.initialize(self.state.makeObservation(1).data)
if not self.informAgentsOfGameStart():
return
agentIndex = self.state.getNextAgentIndex()
numAgents = len( self.agents )
while not self.gameOver:
event = self.state.getNextEvent()
if event.isAgentMove():
agentIndex = event.getAgentIndex()
if not self.getAndRunAgentAction(agentIndex):
return
self.numMoves += 1
else:
self.runEvent()
# 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)
if _BOINC_ENABLED:
boinc.set_fraction_done(self.getProgress())
if not self.informLearningAgents():
return
self.display.finish()
def run(self):
"""
Main control loop for game play.
"""
self.display.initialize(self.state.data)
self.numMoves = 0
agentIndex = self.startingIndex
numAgents = len(self.agents)
previous_action = Directions.STOP
expout = int(self.rules.getMoveTimeout(agentIndex))
totalComputationTime = 0
totalExpandedNodes = 0
if (expout > 0):
pacmodule.pacman.GameState.setMaximumExpanded(expout)
while not self.gameOver:
# Fetch the next agent
agent = self.agents[agentIndex]
move_time = 0
skip_action = False
# Generate an observation of the state
observation = self.state.deepCopy()
# Solicit an action
action = None
self.mute(agentIndex)
pacmodule.pacman.GameState.resetNodeExpansionCounter()
violated = False
t = time.time()
if expout == 0:
action = agent.get_action(observation)
else:
#TODO : node expansion control through getSuccessors
action = agent.get_action(observation)
if pacmodule.pacman.GameState.countExpanded > expout:
violated = True
totalComputationTime += (time.time() - t)
totalExpandedNodes += pacmodule.pacman.GameState.countExpanded
if not self.state.isLegalAction(agentIndex, action):
print("Illegal move !")
action = previous_action
elif violated:
print("Node expansion budget violated !")
action = previous_action
if not self.state.isLegalAction(agentIndex,action):
action = Directions.STOP
self.unmute()
# Execute the action
self.moveHistory.append((agentIndex, action))
previous_action = action
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())
totalScore = self.state.getScore()
self.display.finish()
return totalScore,totalComputationTime,totalExpandedNodes
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 >> sys.stderr, "Agent %d failed to load" % i
self.unmute()
self._agentCrash(i, quiet=True)
return
if ("registerInitialState" in dir(agent)):
self.mute(i)
agent.registerInitialState(self.state.deepCopy())
self.unmute()
agentIndex = self.startingIndex
numAgents = len(self.agents)
while not self.gameOver:
action = None
observation = self.state.deepCopy()
if agentIndex == 0:
# 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)
observation = agent.observationFunction(
self.state.deepCopy())
self.unmute()
else:
observation = self.state.deepCopy()
# 'Fix' observation
ppos = observation.getPacmanPosition()
rpos = observation.getGhostState(1).getPosition()
if manhattanDistance(ppos, rpos) > 12:
observation.removeAgentState(1)
vis_capsules = []
for c in observation.data.capsules:
if manhattanDistance(ppos, c) <= 12:
vis_capsules.append(c)
observation.data.capsules = vis_capsules
# print(observation)
# Solicit an action
self.mute(agentIndex)
start = time.time()
action = agent.getAction(observation)
if time.time() - start > 3.1:
print('Step time exceeds maximum time!')
self.unmute()
self.socket.send(self.packAction(action))
else:
action = self.unpackAction(self.saveRead(4))
# Execute the action
self.moveHistory.append((agentIndex, action))
self.state = self.state.generateSuccessor(agentIndex, action)
observation.data._agentMoved = self.state.data._agentMoved
# Change the display
self.display.update(self.state.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, data:
if not self.catchExceptions: raise
self._agentCrash(agentIndex)
self.unmute()
return
class Game:
"""
The Game manages the control flow, soliciting actions from agents.
"""
def __init__( self, agents, display, rules, startingIndex=0, muteAgents=False ):
self.agentCrashed = False
self.agents = agents
self.display = display
self.rules = rules
self.startingIndex = startingIndex
self.gameOver = False
self.muteAgents = muteAgents
self.catchExceptions = _BOINC_ENABLED
self.moveHistory = []
def getProgress(self):
if self.gameOver:
return 1.0
else:
return self.rules.getProgress(self)
def _agentCrash( self, agentIndex ):
"Helper method for handling agent crashes"
self.gameOver = True
self.agentCrashed = True
self.rules.agentCrash(agentIndex)
OLD_STDOUT = None
OLD_STDERR = None
def mute(self):
if not self.muteAgents: return
global OLD_STDOUT, OLD_STDERR
import cStringIO
OLD_STDOUT = sys.stdout
OLD_STDERR = sys.stderr
sys.stdout = cStringIO.StringIO()
sys.stderr = cStringIO.StringIO()
def unmute(self):
if not self.muteAgents: return
global OLD_STDOUT, OLD_STDERR
sys.stdout.close()
sys.stderr.close()
# 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:
# this is a null agent, meaning it failed to load
# the other team wins
self._agentCrash(i)
return
if ("registerInitialState" in dir(agent)):
self.mute()
agent.registerInitialState(self.state.deepCopy())
self.unmute()
agentIndex = self.startingIndex
numAgents = len( self.agents )
while not self.gameOver:
# Fetch the next agent
agent = self.agents[agentIndex]
# Generate an observation of the state
if 'observationFunction' in dir( agent ):
try:
self.mute()
observation = agent.observationFunction(self.state.deepCopy())
self.unmute()
except Exception,data:
if not self.catchExceptions: raise data
self.unmute()
print "Exception",data
self._agentCrash(agentIndex)
return
else:
observation = self.state.deepCopy()
# Solicit an action
startTime = time.time()
try:
self.mute()
action = agent.getAction( observation )
self.unmute()
self.moveHistory.append( (agentIndex, action) )
except Exception,data:
if not self.catchExceptions: raise data
self.unmute()
print "Exception", data
self._agentCrash(agentIndex)
return
if 'checkTime' in dir(self.rules):
self.rules.checkTime(time.time() - startTime)
# Execute the action
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())
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:
# this is a null agent, meaning it failed to load
# the other team wins
self._agentCrash(i, quiet=True)
return
if ("registerInitialState" in dir(agent)):
self.mute()
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)
self.unmute()
self.agentTimeout = True
self._agentCrash(i, quiet=True)
return
except(Exception,data):
self.unmute()
self._agentCrash(i, quiet=True)
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()
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,data):
self.unmute()
self._agentCrash(agentIndex, quiet=True)
return
else:
observation = agent.observationFunction(self.state.deepCopy())
self.unmute()
else:
observation = self.state.deepCopy()
# Solicit an action
action = None
self.mute()
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)
self.agentTimeout = True
self.unmute()
self._agentCrash(agentIndex, quiet=True)
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]))
if self.totalAgentTimeWarnings[agentIndex] > self.rules.getMaxTimeWarnings(agentIndex):
print("Agent %d exceeded the maximum number of warnings: %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex]))
self.agentTimeout = True
self.unmute()
self._agentCrash(agentIndex, quiet=True)
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]))
self.agentTimeout = True
self.unmute()
self._agentCrash(agentIndex, quiet=True)
return
self.unmute()
except(Exception,data):
self.unmute()
self._agentCrash(agentIndex)
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,data):
self._agentCrash(agentIndex)
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 agent in self.agents:
if "final" in dir( agent ) :
try:
self.mute()
agent.final( self.state )
self.unmute()
except(Exception,data):
if not self.catchExceptions: raise
self.unmute()
print("Exception",data)
self._agentCrash(agent.index)
return
self.display.finish()
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()
"Import classes from busteresAgents"
from bustersAgents import BasicAgentAA
from bustersAgents import BustersAgent
from keyboardAgents import KeyboardAgent
import os.path
import arff
from os import path
"Check if .arff file exists, if yes than just open it, if no, than create"
if path.exists("all_data_pacman.arff") is True:
print('Results file exists')
f = open("all_data_pacman.arff", "a")
d = list()
else:
f = open("all_data_pacman.arff", "a")
f.write(
"@relation Example1 \n@attribute PosX numeric \n@attribute PosY numeric \n@attribute isNorthLegal {True, False} \n@attribute isEastLegal {True, False} \n@attribute isSouthLegal {True, False} \n@attribute isWestLegal {True, False} \n@attribute Direction {Stop, North, South, East, West} \n@attribute Ghost1X numeric \n@attribute Ghost1Y numeric \n@attribute Ghost1Distance numeric \n@attribute Ghost2X numeric \n@attribute Ghost2Y numeric \n@attribute Ghost2Distance numeric \n@attribute Ghost3X numeric \n@attribute Ghost3Y numeric \n@attribute Ghost3Distance numeric \n@attribute Ghost4X numeric \n@attribute Ghost4Y numeric \n@attribute Ghost4Distance numeric \n@attribute NumberOfDots numeric \n@attribute NearestDotDistance numeric \n@attribute CurrentScore numeric \n@attribute FutureScore numeric \n@attribute FutureDirection {Stop, North, South, East, West} \n@data \n"
)
print('Created new results file')
agentIndex = self.startingIndex
numAgents = len(self.agents)
step = 0
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()
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Change it
if agentIndex == self.startingIndex:
if step >= 1:
f.write(str(observation.getScore()) + ',')
f.write(
str(observation.data.agentStates[0].getDirection()) +
'\n')
g = BasicAgentAA.printLineData(self, observation)
if g:
for i in g:
if i == None:
i = '?'
f.write(str(i) + ',')
# Solicit an action
action = None
step += 1
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())
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
self._agentCrash(agentIndex)
self.unmute()
return
f.write("0" + ',' + 'Stop' + '\n')
f.close()
self.display.finish()
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 cStringIO
self.agentOutput = [cStringIO.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 cStringIO
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 >> sys.stderr, "Agent %d failed to load" % i
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 >> sys.stderr, "Agent %d ran out of time on startup!" % i
self.unmute()
self.agentTimeout = True
self._agentCrash(i, quiet=True)
return
except Exception, 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
oneStepActionsList = []
for i in range(4):
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, 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 >> sys.stderr, "Agent %d timed out on a single move!" % agentIndex
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 >> sys.stderr, "Agent %d took too long to make a move! This is warning %d" % (
agentIndex,
self.totalAgentTimeWarnings[agentIndex])
if self.totalAgentTimeWarnings[
agentIndex] > self.rules.getMaxTimeWarnings(
agentIndex):
print >> sys.stderr, "Agent %d exceeded the maximum number of warnings: %d" % (
agentIndex,
self.totalAgentTimeWarnings[agentIndex])
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 >> sys.stderr, "Agent %d ran out of time! (time: %1.2f)" % (
agentIndex, self.totalAgentTimes[agentIndex])
self.agentTimeout = True
self._agentCrash(agentIndex, quiet=True)
self.unmute()
return
self.unmute()
except Exception, data:
self._agentCrash(agentIndex)
self.unmute()
return
else:
action = agent.getAction(observation)
self.unmute()
# Execute the action
self.moveHistory.append((agentIndex, action))
oneStepActionsList.append((agentIndex, action))
'''
if self.catchExceptions:
try:
self.state = self.state.generateSuccessor( agentIndex, action )
except Exception,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())
def run(self):
self.display.initialize(self.state.data)
self.num_moves = 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)
sys.stderr.write("Agent %d failed to load" % i)
self.unmute()
self._agent_crash(i, quiet=True)
return
if "register_initial_state" in dir(agent):
self.mute(i)
if self.catch_exceptions:
try:
timed_func = TimeoutFunction(
agent.register_initial_state,
int(self.rules.get_max_startup_time(i)))
try:
start_time = time.time()
timed_func(self.state.deep_copy())
time_taken = time.time() - start_time
self.total_agent_times[i] += time_taken
except TimeoutFunctionException:
sys.stderr.write(
"Agent %d ran out of time on startup!\n" % i)
self.unmute()
self.agent_timeout = True
self._agent_crash(i, quiet=True)
return
except Exception:
self._agent_crash(i, quiet=False)
self.unmute()
return
else:
agent.register_initial_state(self.state.deep_copy())
self.unmute()
agent_index = self.starting_index
num_agents = len(self.agents)
while not self.game_over:
# Fetch the next agent.
agent = self.agents[agent_index]
move_time = 0
skip_action = False
# Generate an observation of the state.
if 'observation_function' in dir(agent):
self.mute(agent_index)
if self.catch_exceptions:
try:
timed_func = TimeoutFunction(
agent.observation_function,
int(self.rules.get_move_timeout(agent_index)))
try:
start_time = time.time()
observation = timed_func(self.state.deep_copy())
except TimeoutFunctionException:
skip_action = True
move_time += time.time() - start_time
self.unmute()
except Exception:
self._agent_crash(agent_index, quiet=False)
self.unmute()
return
else:
observation = agent.observation_function(
self.state.deep_copy())
self.unmute()
else:
observation = self.state.deep_copy()
# Solicit an action.
action = None
self.mute(agent_index)
if self.catch_exceptions:
try:
timed_func = TimeoutFunction(
agent.get_action,
int(self.rules.get_move_timeout(agent_index)) -
int(move_time))
try:
start_time = time.time()
if skip_action:
raise TimeoutFunctionException()
action = timed_func(observation)
except TimeoutFunctionException:
sys.stderr.write(
"Agent %d timed out on a single move!\n" %
agent_index)
self.agent_timeout = True
self._agent_crash(agent_index, quiet=True)
self.unmute()
return
move_time += time.time() - start_time
if move_time > self.rules.get_move_warning_time(
agent_index):
self.total_agent_time_warnings[agent_index] += 1
sys.stderr.write(
"Agent %d took too long to make a move! This is warning %d\n"
% (agent_index,
self.total_agent_time_warnings[agent_index]))
if self.total_agent_time_warnings[
agent_index] > self.rules.get_max_time_warnings(
agent_index):
sys.stderr.write(
"Agent %d exceeded the maximum number of warnings: %d\n"
%
(agent_index,
self.total_agent_time_warnings[agent_index]))
self.agent_timeout = True
self._agent_crash(agent_index, quiet=True)
self.unmute()
return
self.total_agent_times[agent_index] += move_time
if self.total_agent_times[
agent_index] > self.rules.get_max_total_time(
agent_index):
sys.stderr.write(
"Agent %d ran out of time! (time: %1.2f)\n" %
(agent_index, self.total_agent_times[agent_index]))
self.agent_timeout = True
self._agent_crash(agent_index, quiet=True)
self.unmute()
return
self.unmute()
except Exception as data:
self._agent_crash(agent_index)
self.unmute()
return
else:
action = agent.get_action(observation)
self.unmute()
# Execute the action.
self.move_history.append((agent_index, action))
if self.catch_exceptions:
try:
self.state = self.state.generate_successor(
agent_index, action)
except Exception:
self.mute(agent_index)
self._agent_crash(agent_index)
self.unmute()
return
else:
self.state = self.state.generate_successor(agent_index, 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 agent_index == num_agents + 1:
self.num_moves += 1
# Next agent.
agent_index = (agent_index + 1) % num_agents
if _BOINC_ENABLED:
boinc.set_fraction_done(self.get_progress())
# Inform a learning agent of the game result.
for agent_index, agent in enumerate(self.agents):
if "final" in dir(agent):
try:
self.mute(agent_index)
agent.final(self.state)
self.unmute()
except Exception:
if not self.catch_exceptions:
raise
self._agent_crash(agent_index)
self.unmute()
return
self.display.finish()
def run(self):
"""
Main control loop for game play.
"""
self.inform_agent_about_start()
self.display.initialize(self.state.data)
self.numMoves = 0
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 >> sys.stderr, "Agent %d failed to load" % i
self.unmute()
self._agentCrash(i, quiet=True)
return
agentIndex = self.startingIndex
numAgents = len(self.agents)
pool = Pool(processes=4)
while not self.gameOver:
# Fetch the next agent
# agent = self.agents[agentIndex]
# move_time = 0
# skip_action = False
# Generate an observation of the state
# observation = self.state.deepCopy()
# Solicit an action
action = None
self.mute(agentIndex)
gameStateDTO = PublicGameState(self.state.deepCopy())
myFutureResults = pool.imap_unordered(
choose_action, [(x, gameStateDTO, self.agents[x].ip_address)
for x in range(numAgents)])
myResults = []
for agentIndex in range(numAgents):
try:
myResults.append(myFutureResults.next(1))
except Exception as e:
print("Agent %d reached timeout" % agentIndex)
myResults.append(
(agentIndex,
json.dumps(
random.choice(
self.state.getLegalActions(agentIndex)))))
for myResult in myResults:
agentIndex = myResult[0]
try:
action = json.loads(myResult[1])
except Exception as dat:
print("Have to choose a random action for agent %d" %
agentIndex)
action = random.choice(
self.state.getLegalActions(agentIndex))
# 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)
# Allow for game specific conditions (winning, losing, etc.)
self.rules.process(self.state, self)
# Track progress
self.numMoves += 1
# Allow for game specific conditions (winning, losing, etc.)
self.rules.process(self.state, self)
# Track progress
self.numMoves += 1
# Next agent
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
self._agentCrash(agentIndex)
self.unmute()
return
score = self.state.data.score
if score == 0:
messagebox.showinfo("No Winner", "There is no Winner")
if score > 0:
messagebox.showinfo("Red Wins",
"Team Red wins with score: " + str(score))
if score < 0:
messagebox.showinfo(
"Blue Wins", "Team Blue wins with score: " + str(score * -1))
self.display.finish()
# game.py
def run(self):
"""
Main control loop for game play.
"""
self.display.initialize(self.state.data)
self.numMoves = 0
from graphicsDisplay import PacmanGraphics
if self.createPolicy and 'initPolicy' in dir(self.display):
policy = self.display.initPolicy(self, 0) # assume 0 is player
return policy, self
###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 >> sys.stderr, "Agent %d failed to load" % i
self.unmute()
self._agentCrash(i, quiet=True)
return
if ("registerInitialState" in dir(agent)):
agent.registerInitialState(self.state.deepCopy())
agentIndex = self.startingIndex
numAgents = len(self.agents)
while not self.gameOver:
# Fetch the next agent
agent = self.agents[agentIndex]
move_time = 0
# Generate an observation of the state
if 'observationFunction' in dir(agent):
observation = agent.observationFunction(self.state.deepCopy())
else:
observation = self.state.deepCopy()
# Solicit an action
action = None
while action == None:
action = agent.getAction(observation)
time.sleep(0.1)
# Execute the action
self.moveHistory.append((agentIndex, action))
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, data:
raise
self._agentCrash(agentIndex)
self.unmute()
return
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()
# print("registered...") #whoami
agentIndex = self.startingIndex
numAgents = len( self.agents )
maihoonnaa=1 #whoami
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))
timed_func = TimeoutFunction(agent.getAction, int(self.state.data.score))
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:
# try:
# timed_func = TimeoutFunction(agent.getAction, int(math.ceil(self.state.data.score / (SCALING_FACTOR+1))))
# try:
# start_time = time.time()
# action = timed_func(observation)
# except TimeoutFunctionException:
# print('You have run out of compute time! You exceeded {:.3f}s of compute'.format(self.state.data.score / (SCALING_FACTOR+1)))
# self.state.data.score = 0
# self.state.data._lose = True
# self.rules.process(self.state, self)
# continue
# # except:
# # print('Your agent crashed!')
# # self.state.data.score = 0
# # self.state.data._lose = True
# # self.rules.process(self.state, self)
# # continue
# move_time = time.time() - start_time
action = agent.getAction(self.state.deepCopy())
assert(action in self.state.getLegalActions(agentIndex)), str(self.state) + " "+ str(self.state.getLegalActions(agentIndex)) +" "+str(action) +" " + str(agentIndex)
#whoami
self.unmute()
self.state.data.score += 0 #whoami max(0,-1 * SCALING_FACTOR)
# if self.state.data.score <= 0:
# self.state.data.score = 0
# self.state.data._lose = True
# self.rules.process(self.state, self)
# continue #whoami
# if self.state.data.deathCount >= 2:
# self.state.data._lose = True
# self.rules.process(self.state, self)
# continue
# #whoami
# 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.)
# print("death counter now ",self.state.data.deathCount, agentIndex)#whoami
self.rules.process(self.state, self)
# print("step ",maihoonnaa,"...",agentIndex) #whoami
maihoonnaa+=1#whoami
# 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
# print("terminal...") #whoami
self.display.finish()
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()
"Import classes from busteresAgents"
from bustersAgents import BasicAgentAA
from bustersAgents import BustersAgent
import os.path
from os import path
"Check if csv file exists, if yes than just open it, if no, than create"
# assign header columns
headerList = [
'PositionX', 'PositionY', 'is North legal', 'is East legal',
'is South legal', 'is West legal', 'is Stop legal', 'Direction',
'Position', 'Ghost1 X', 'Ghost1 Y', 'Ghost2 X', 'Ghost2 Y',
'Ghost3 X', 'Ghost3 Y', 'Ghost4 X', 'Ghost4 Y'
]
if path.exists("Results.csv") is True:
print('Results file exists')
csvfile = open('Results.csv', 'a', newline='')
spamwriter = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow(['', '', '', '', '', '', '', ''])
else:
# open CSV file and assign header
csvfile = open("Results.csv", 'w')
dw = csv.DictWriter(csvfile, delimiter=',', fieldnames=headerList)
dw.writeheader()
print('Created new results file')
agentIndex = self.startingIndex
numAgents = len(self.agents)
step = 0
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
step += 1
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())
#USE MY FUNCTION PRINT LINE FROM BASIC AGENT
g = BasicAgentAA.printLineData(self, observation)
"Writing results to file"
spamwriter = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow(g)
# 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
self._agentCrash(agentIndex)
self.unmute()
return
self.display.finish()
def run(self):
"""
Main control loop for game play.
"""
self.display.initialize(self.state.data)
self.numMoves = 0
# Simple way to determine whether we are in phase 3
# as there will be a ghost
phase3 = len(self.agents) > 2
initialBroadcast = None
# Inform learning agents of the game start
for i in range(len(self.agents)):
agent = self.agents[i]
#agent = None
if not agent:
self.mute(i)
# If null agent, it failed to load. Terminate the game
print("Agent %d failed to load" % i, file=sys.stderr)
self.unmute()
self._agentCrash(i, quiet=True)
return
# Hack to get broadcast across to index 1 agent
# during registerInitialState for phase 2
if not phase3 and initialBroadcast is not None:
agent.receivedInitialBroadcast = initialBroadcast
initialBroadcast = None
if ("registerInitialState" in dir(agent)):
self.mute(i)
# Timed register initial state
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
# Agent initialization times
# print(self.totalAgentTimes)
except Exception as data:
self._agentCrash(i, quiet=False)
self.unmute()
return
else:
agent.registerInitialState(self.state.deepCopy())
# Part 2 of hack to get agent 0 to broadcast path
# to agent 1 during registerInitialState for phase 2
if not phase3 and i == 0:
initialBroadcast = agent.toInitialBroadcast
assert initialBroadcast is None or all(
[a in LEGAL_DIRECTIONS for a in initialBroadcast])
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)
# Get other pacman on the agent's team
teammateList = [
self.agents[i] for i in agent.getTeam(observation)
if i != agent.index
]
# Only broadcast/receive broadcast if has teammates, i.e. is not a ghost
if len(teammateList) > 0:
otherPacman = teammateList[0]
broadcast = agent.toBroadcast
assert broadcast is None or all(
[a in LEGAL_DIRECTIONS for a in broadcast])
otherPacman.receivedBroadcast = broadcast
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)
# 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
self._agentCrash(agentIndex)
self.unmute()
return
self.display.finish()
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
self._agentCrash(agentIndex)
self.unmute()
return
self.display.finish()
def run( self,EPISODES,callbacks=[],log_dir="" ):
"""
Main control loop for game play.
"""
self.agents[0].last_episode = EPISODES
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 >>sys.stderr, "Agent %d failed to load" % i
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 >>sys.stderr, "Agent %d ran out of time on startup!" % i
self.unmute()
self.agentTimeout = True
self._agentCrash(i, quiet=True)
return
except Exception:
# aquí habia variable 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 )
# TODO dejo esto aquí pues es donde empieza
self.agents[0].n =0
res = 0
m = 0
while not self.gameOver:
# Fetch the next agent
agent = self.agents[agentIndex]
mov = 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:
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 >>sys.stderr, "Agent %d timed out on a single move!" % agentIndex
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 >>sys.stderr, "Agent %d took too long to make a move! This is warning %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex])
if self.totalAgentTimeWarnings[agentIndex] > self.rules.getMaxTimeWarnings(agentIndex):
print >>sys.stderr, "Agent %d exceeded the maximum number of warnings: %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex])
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 >>sys.stderr, "Agent %d ran out of time! (time: %1.2f)" % (agentIndex, self.totalAgentTimes[agentIndex])
self.agentTimeout = True
self._agentCrash(agentIndex, quiet=True)
self.unmute()
return
self.unmute()
except Exception:
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:
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())
res += 1/(m+1)*(self.agents[0].lastReward-res)
m += 1
# print(f"Episodio: {EPISODES:d}")
# inform a learning agent of the game result
#TODO aquí aparecen cuando se acaban los juegos
for agentIndex, agent in enumerate(self.agents):
if "final" in dir( agent ) :
try:
self.mute(agentIndex)
agent.final( self.state )
if not agent.prueba:
agent.policy_second.update_policy(agent,callbacks,log_dir=log_dir)
self.unmute()
except Exception:
if not self.catchExceptions: raise
self._agentCrash(agentIndex)
self.unmute()
return
self.display.finish()
return self.agents[0].lastReward,self.agents[0].epsilon,self.agents[0].phi
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, data:
if not self.catchExceptions: raise
self._agentCrash(agentIndex)
self.unmute()
return
self.display.finish()
def run(self, replay, minibatch_size): #whoami
"""
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()
# print("registered...") #whoami
####################
old_score = 100
#####################
# agentIndex = self.startingIndex
# numAgents = len( self.agents )
maihoonnaa = 1 #whoami
while not self.gameOver:
# Fetch the next agent
# agent = self.agents[agentIndex]
# move_time = 0
# skip_action = False
act_vect = []
action_list = []
reward_vect = []
tab_ka_state = self.state
for i in range(len(self.agents)):
action = self.agents[i].getAction(self.state.deepCopy())
assert (action in self.state.getLegalActions(i)), str(
self.state) + " " + str(self.state.getLegalActions(
i)) + " " + str(action) + " " + str(i)
action_list.append(action)
if (self.state.data.agentStates[i].isPacman):
act_vect.append(action)
# print(tab_ka_state,"tab_ka_state") ; print(action_list,"yahan")
old_state = self.state.deepCopy()
#print(self.state,"wahan")
for i in range(len(self.agents)):
if (self.state.data._win or self.state.data._lose):
# self.state=self.state.generateSuccessor( i, action_list[i] ) #agentIndex, action
# reward_vect.append(self.state.data.score-old_score)
# old_score=self.state.data.score
# print(self.state,"kahan2")
# if(self.state.data.agentStates[i].isPacman):
# # print("udhar",i)
# reward_vect.append(0)
# continue
for o in range(len(reward_vect)):
if (self.state.data._win):
reward_vect[o] = 500
else:
reward_vect[o] = -500
h = len(reward_vect)
for o in range(self.state.data.numPacmanAgents - h):
if (self.state.data._win):
reward_vect.append(500)
else:
reward_vect.append(-500)
break
try:
self.moveHistory.append((i, action_list[i]))
self.state = self.state.generateSuccessor(
i, action_list[i]) #agentIndex, action
except Exception as e:
if (str(e) ==
'Can\'t generate a successor of a terminal state.'
):
print("this shouldnt happen")
raise Exception(str(e))
elif (str(e) != "Illegal ghost action "):
print("this too shouldnt happen")
raise Exception(str(e))
self.display.update(self.state.data)
self.rules.process(self.state, self)
if (self.state.data.agentStates[i].isPacman):
# print("idhar",i)
reward_vect.append(self.state.data.score - old_score)
else:
r = self.state.data.score - old_score
ghostState = self.state.data.agentStates[i]
ghostPosition = ghostState.configuration.getPosition()
for j in range(self.state.data.numPacmanAgents):
pacmanPosition = self.state.getPacmanPosition(j)
if (manhattanDistance(ghostPosition, pacmanPosition) <=
COLLISION_TOLERANCE):
reward_vect[j] += (r /
self.state.data.numPacmanAgents)
old_score = self.state.data.score
assert (len(reward_vect) == self.state.data.numPacmanAgents
), "reward vector flawed" + str(reward_vect)
f = open("actions.txt", "a")
f.write("replay ENTRY:\n")
f.write(str(old_state.deepCopy()) + "\n")
f.write(
str(tuple(copy.deepcopy(act_vect))) + " " +
str(tuple(copy.deepcopy(reward_vect))) + "\n")
f.write(str(self.state.deepCopy()) + "\n")
f.close()
replay[(old_state.deepCopy(), tuple(copy.deepcopy(act_vect)),
tuple(copy.deepcopy(reward_vect)),
self.state.deepCopy())] = 1
#------------------------------------------------------
for i in range(len(self.agents)):
holdit = replay.keys()
points = np.random.choice(
[nn for nn in range(len(list(holdit)))],
min(minibatch_size, len(holdit)), False)
samples = []
for t in range(points.shape[0]):
samples.append(
list(holdit)[points[t]]) #tuples are appended here
for k in range(len(samples)):
(s, a, r, s_n) = samples[k]
a = list(a)
r = list(r)
agent = self.agents[i] #i=agentIndex
# Generate an observation of the state
if 'observationFunction' in dir(agent):
self.mute(i)
if self.catchExceptions:
try:
timed_func = TimeoutFunction(
agent.observationFunction,
int(self.rules.getMoveTimeout(agentIndex)))
try:
start_time = time.time()
observation = timed_func(
s_n.deepCopy(), s.deepCopy(),
copy.deepcopy(r), copy.deepcopy(a), k)
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(
s_n.deepCopy(), s.deepCopy(), copy.deepcopy(r),
copy.deepcopy(a), k)
self.unmute()
else:
observation = self.state.deepCopy()
if (self.state.data.agentStates[i].isPacman):
agent.update_the_params(len(samples))
# # Generate an observation of the state
# if 'observationFunction' in dir( agent ):
# self.mute(i)
# if self.catchExceptions:
# try:
# timed_func = TimeoutFunction(agent.observationFunction, int(self.rules.getMoveTimeout(agentIndex)))
# try:
# start_time = time.time()
# observation = timed_func(s_n.deepCopy(),s.deepCopy(),copy.deepcopy(r),copy.deepcopy(a))
# 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(s_n.deepCopy(),s.deepCopy(),copy.deepcopy(r),copy.deepcopy(a))
# self.unmute()
# else:
# observation = self.state.deepCopy()
################################### REMOVED AREA
# 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.)
# # print("death counter now ",self.state.data.deathCount, agentIndex)#whoami
# self.rules.process(self.state, self)
# print("maihoonnaa ",maihoonnaa,"...") #whoami
maihoonnaa += 1 #whoami
# 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)
#whoami
# agent.final( self.state )
holdit = replay.keys()
points = np.random.choice(
[nn for nn in range(len(list(holdit)))],
min(minibatch_size, len(holdit)), False)
samples = []
for t in range(points.shape[0]):
samples.append(
list(holdit)[points[t]]) #tuples are appended here
for k in range(len(samples)):
(s, a, r, s_n) = samples[k]
a = list(a)
r = list(r)
observation = agent.observationFunction(
s_n.deepCopy(), s.deepCopy(), copy.deepcopy(r),
copy.deepcopy(a), k)
if (self.state.data.agentStates[agentIndex].isPacman):
agent.final(s_n.deepCopy(), len(samples))
self.unmute()
except Exception as data:
if not self.catchExceptions: raise data
self._agentCrash(agentIndex)
self.unmute()
return
# print("terminal...") #whoami
self.display.finish()
return replay
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 agent in self.agents:
if "final" in dir( agent ) :
try:
self.mute(agent.index)
agent.final( self.state )
self.unmute()
except Exception,data:
if not self.catchExceptions: raise
self._agentCrash(agent.index)
self.unmute()
return
self.display.finish()
def run(self, total_pacmen, pacman_types_corresponding_indexes, graphics,
pacmen, stillTraining, is_training, numGames, evalGraphics,
currentRound, numTraining):
"""
Main control loop for game play.
"""
self.display.initialize(self.state.data, total_pacmen,
pacman_types_corresponding_indexes)
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(), i,
type(agent).__name__,
pacman_types_corresponding_indexes, graphics,
is_training, evalGraphics, numGames)
# TODO: could this exceed the total time
self.unmute()
agentIndex = self.startingIndex
numAgents = len(self.agents)
if graphics or evalGraphics:
self.display.updateEpochInfo(currentRound, numTraining)
while not self.gameOver:
# Fetch the next agent
agent = self.agents[agentIndex]
# if this is a dead pacman, skip loading it
if agent.isPacman == True:
if agent.isDead == True:
# Next agent
agentIndex = (agentIndex + 1) % numAgents
continue
agent.scoreChange = 0
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(), total_pacmen, agentIndex,
stillTraining)
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, total_pacmen, agentIndex)
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, total_pacmen, pacmen, agent)
# check if there's dead pacman in the current round
deadPacman = None
deadPacmanIndex = None
if self.state.data.deadPacmanIndex != None:
# inform this learning agent of the game result
deadPacmanIndex = self.state.data.deadPacmanIndex
deadPacman = pacmen[deadPacmanIndex]
if "final" in dir(deadPacman):
try:
self.mute(deadPacmanIndex)
forceFinish = False
deadPacman.final(self.state, total_pacmen,
deadPacmanIndex, stillTraining,
forceFinish)
# update its death "reward"
pacmenScoreChanges[
deadPacmanIndex] = deadPacman.scoreChange
if 'observationFunction' in dir(deadPacman):
observation = deadPacman.observationFunction(
self.state.deepCopy(), total_pacmen,
deadPacmanIndex, stillTraining)
self.unmute()
except Exception as data:
if not self.catchExceptions:
raise
self._agentCrash(deadPacmanIndex)
self.unmute()
return
# Allow for game specific conditions (winning, losing, etc.)
self.rules.process(self.state, self)
# Change the display
# now also remove the dead pacman from the screen
self.display.update(self.state.data, total_pacmen, agent,
agentIndex, deadPacman, deadPacmanIndex,
currentRound, numTraining)
###idx = agentIndex - agentIndex % 2 + 1
###self.display.update( self.state.makeObservation(idx).data )
# update pacman scoreChange
if self.state.data.deadPacmanIndex == None:
if agent.isPacman == True or self.state.data.collidedPacman != None:
if agentIndex < total_pacmen:
# current agent is pacman
pacmenScoreChanges[agentIndex] = agent.scoreChange
else:
collidedPacmanIndex = self.state.data.collidedPacman
pacmenScoreChanges[collidedPacmanIndex] = self.agents[
collidedPacmanIndex].scoreChange
self.state.data.collidedPacman = None
self.state.deadPacmanIndex = None
# Track progress
if agentIndex == numAgents + 1:
self.numMoves += 1
# Next agent
agentIndex = (agentIndex + 1) % numAgents
if _BOINC_ENABLED:
boinc.set_fraction_done(self.getProgress())
# Only used for ghosts after changing to end the game iff all pacmen die
for agentIndex, agent in enumerate(self.agents):
if agent.isPacman == False:
if "final" in dir(agent):
try:
self.mute(agentIndex)
agent.final(self.state, total_pacmen, agentIndex)
self.unmute()
except Exception as data:
if not self.catchExceptions:
raise
self._agentCrash(agentIndex)
self.unmute()
return
else:
continue
# if already reach to the end of training episodes, finish training for those Pacmen who didn't get a chance to be trained(usually this happs if -x too small)
if currentRound == numTraining:
for pacman in pacmen:
if pacman.hasFinishedTraining == False:
forceFinish = True
pacman.final(self.state, total_pacmen, pacman.index,
stillTraining, forceFinish)
self.display.finish()