def final(self, gameState):
print("Weights", self.index, ":")
print(self.getWeights())
self.gameNumber += 1
self.alpha -= self.alphaDecay
self.alpha = max(self.alpha, self.minAlpha)
CaptureAgent.final(self, gameState)
def final(self, state):
"Called at the end of each game."
# call the super-class final method
CaptureAgent.final(self, state)
#if self.isTraining() and DEBUG:
#print "END WEIGHTS"
#print self.weights
self.episodesSoFar += 1
def final(self, gameState):
print("\nWeights defensive")
print(self.getWeights())
print()
self.gameNumber += 1
self.alpha -= self.alphaDecay
self.alpha = max(self.alpha, self.minAlpha)
CaptureAgent.final(self, gameState)
def final(self, state):
"Called at the end of each game."
# call the super-class final method
CaptureAgent.final(self, state)
if self.isTraining() and DEBUG:
print "END WEIGHTS"
print self.weights
self.episodesSoFar += 1
if self.episodesSoFar == self.numTraining:
print "FINISHED TRAINING"
def final(self, gameState):
self.stopEpisode()
if self.isInTraining():
self.makeUpdate()
print 'Training:{'
else:
print 'Testing:{'
print self.getScore(self.getCurrentObservation())
print '}'
CaptureAgent.final(self, gameState)
def final(self, gameState):
# update on last movement
rewardChange = (gameState.getScore() - self.lastState.getScore())
self.observeTransition(self.lastState, self.lastAction, gameState,
rewardChange)
#clear observation history list
CaptureAgent.final(self, gameState)
# stop episode
self.endOneEpisode()
print(gameState)
def final(self, gameState):
"""
Receive final game state
"""
CaptureAgent.final(self, gameState)
if TRAIN:
for i, (weight, bias) in enumerate(self._omodel):
np.save("oweight%d" % i, weight)
np.save("obias%d" % i, bias)
for i, (weight, bias) in enumerate(self._dmodel):
np.save("dweight%d" % i, weight)
np.save("dbias%d" % i, bias)
for i, (weight, bias) in enumerate(self._fmodel):
np.save("fweight%d" % i, weight)
np.save("fbias%d" % i, bias)
def final(self, state):
"Called at the end of each game."
self.observationFunction(state)
CaptureAgent.final(self, state)
self.currentEpisode += 1
print("Episode number: %d" % (self.currentEpisode))
print("Episode rewards: %d" % (self.episodeRewards))
self.episodeRewards = 0
print(self.weights)
if self.currentEpisode == self.numTraining:
print
"FINISHED TRAINING"
with open('weights' + str(self.index), 'wb') as fp:
pickle.dump(self.weights, fp)
def final(self, state):
"""
Called by Pacman game at the terminal state
"""
CaptureAgent.final(self, state)
deltaReward = state.getScore() - self.lastState.getScore()
self.observeTransition(self.lastState, self.lastAction, state,
deltaReward)
self.stopEpisode()
# Make sure we have this var
if not 'episodeStartTime' in self.__dict__:
self.episodeStartTime = time.time()
if not 'lastWindowAccumRewards' in self.__dict__:
self.lastWindowAccumRewards = 0.0
self.lastWindowAccumRewards += state.getScore()
NUM_EPS_UPDATE = 100
if self.episodesSoFar % NUM_EPS_UPDATE == 0:
print 'Reinforcement Learning Status:'
windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)
if self.episodesSoFar <= self.numTraining:
trainAvg = self.accumTrainRewards / float(self.episodesSoFar)
print '\tCompleted %d out of %d training episodes' % (
self.episodesSoFar, self.numTraining)
print '\tAverage Rewards over all training: %.2f' % (trainAvg)
else:
testAvg = float(self.accumTestRewards) / (self.episodesSoFar -
self.numTraining)
print '\tCompleted %d test episodes' % (self.episodesSoFar -
self.numTraining)
print '\tAverage Rewards over testing: %.2f' % testAvg
print '\tAverage Rewards for last %d episodes: %.2f' % (
NUM_EPS_UPDATE, windowAvg)
print '\tEpisode took %.2f seconds' % (time.time() -
self.episodeStartTime)
self.lastWindowAccumRewards = 0.0
self.episodeStartTime = time.time()
if self.episodesSoFar == self.numTraining:
msg = 'Training Done (turning off epsilon and alpha)'
print '%s\n%s' % (msg, '-' * len(msg))
print self.getWeights()
def final(self, state):
"""
Called by Pacman game at the terminal state
"""
CaptureAgent.final(self, state)
deltaReward = state.getScore() - self.lastState.getScore()
self.observeTransition(self.lastState, self.lastAction, state, deltaReward)
self.stopEpisode()
# Make sure we have this var
if not 'episodeStartTime' in self.__dict__:
self.episodeStartTime = time.time()
if not 'lastWindowAccumRewards' in self.__dict__:
self.lastWindowAccumRewards = 0.0
self.lastWindowAccumRewards += state.getScore()
NUM_EPS_UPDATE = 100
if self.episodesSoFar % NUM_EPS_UPDATE == 0:
print 'Reinforcement Learning Status:'
windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)
if self.episodesSoFar <= self.numTraining:
trainAvg = self.accumTrainRewards / float(self.episodesSoFar)
print '\tCompleted %d out of %d training episodes' % (
self.episodesSoFar,self.numTraining)
print '\tAverage Rewards over all training: %.2f' % (
trainAvg)
else:
testAvg = float(self.accumTestRewards) / (self.episodesSoFar - self.numTraining)
print '\tCompleted %d test episodes' % (self.episodesSoFar - self.numTraining)
print '\tAverage Rewards over testing: %.2f' % testAvg
print '\tAverage Rewards for last %d episodes: %.2f' % (
NUM_EPS_UPDATE,windowAvg)
print '\tEpisode took %.2f seconds' % (time.time() - self.episodeStartTime)
self.lastWindowAccumRewards = 0.0
self.episodeStartTime = time.time()
if self.episodesSoFar == self.numTraining:
msg = 'Training Done (turning off epsilon and alpha)'
print '%s\n%s' % (msg,'-' * len(msg))
print self.getWeights()
def final(self, gameState):
# print "Warnings count:", self.warnings
CaptureAgent.final(self, gameState)
def final(self, gameState):
CaptureAgent.final(self, gameState)
def final(self, state):
"Called at the end of each game."
# call the super-class final method
CaptureAgent.final(self, state)
def final(self, state):
CaptureAgent.final(self, state)
self.episodesCur += 1
def final(self, state): "Called at the end of each game." # call the super-class final method CaptureAgent.final(self, state) if self.isTraining() and DEBUG: self.episodesSoFar += 1
