def final(self, state):
"Called at the end of each game."
# call the super-class final method
ReinforcementAgent.final(self, state)
#gc.collect() #por si escaso
#del gc.garbage[:] # por si escaso
#print "memory length: ", len(self.memory)
self.new_episode = True
if self.training and state.getScore() > self.best_score:
self.model.save_weights("models/best_model.h5")
self.double_model.save_weights("models/best_double_model.h5")
self.best_score = state.getScore()
print "updated best models for best score: ", self.best_score
if self.episodesSoFar % 1000 == 0 and self.training:
#print "starting memory dump"
# with open('models/memory.dictionary', 'wb') as memory_deque_file:
# pickle.dump(self.memory, memory_deque_file)
# print "finished memory dump"
self.model.save_weights("models/" + str(self.episodesSoFar) +
"model.h5")
self.double_model.save_weights("models/" +
str(self.episodesSoFar) +
"double_model.h5")
# did we finish training?
if self.episodesSoFar == self.numTraining:
# you might want to print your weights here for debugging
"*** YOUR CODE HERE ***"
self.training = False
pass
def final(self, state):
ReinforcementAgent.final(self, state)
print("episode so far: %d" % (self.episodesSoFar))
self.new_episode = True
if self.training and state.getScore() > self.best_score:
self.best_score = state.getScore()
print("updated best first_models for best score: ",
self.best_score)
if self.episodesSoFar == self.numTraining:
self.training = False
pass
def final(self, state):
"Called at the end of each game."
# call the super-class final method
ReinforcementAgent.final(self, state)
if self.episodesSoFar <= self.numTraining:
self.saveQTableToFile('qvalues.pkl')
# did we finish training?
if self.episodesSoFar == self.numTraining:
# you might want to print your weights here for debugging
"*** YOUR CODE HERE ***"
pass
def final(self, state):
# print("Score: {}".format(state.getScore()))
ReinforcementAgent.final(self, state)
# if self.episodesSoFar == self.numTraining:
self.outfile.write("{}\n".format(state.getScore()))
if self.episodesSoFar == self.numTesting:
msg = "Training Done (turning off epsilon and alpha). Beginning Testing..."
print("%s\n%s" % (msg, "-" * len(msg)))
self.epsilon = 0.0 # no exploration
self.alpha = 0.0 # no learning
self.outfile.write("\n")
if self.episodesSoFar == self.numTraining:
msg = "Testing Done."
print("%s\n%s" % (msg, "-" * len(msg)))
def final(self, state):
"""
Shows episode statistics and call superclass final
:param state:
:return:
"""
self.stats['finalScore'] = state.getScore()
self.trace('Final Statictics:')
for stat in enumerate(self.stats):
self.trace(' ' + str(stat[0]) + "/" + str(stat[1]) + "=" +
str(self.stats[stat[1]]))
return ReinforcementAgent.final(self, state)
def final(self, state):
ReinforcementAgent.final(self, state)
if self.episodesSoFar <= self.numTraining:
self.saveTableToFile('featuresqtable.pkl')
