def __init__(self):
Responder.__init__(self)
self.batch_size = 50
self.gamma = 0.99
self.update_frequency = 5
self.learningRate = 0.1
self.createGraph()
def __init__(self):
Responder.__init__(self)
self.numStates = 71
self.numActions = 71
tf.reset_default_graph()
# Set learning parameters
learningRate = 0.1
self.action = 0
self.currentState = 0
self.y = .99
self.e = 0.1
self.numResets = 0
self.resetCalled = False
self.learn = self.runNet()
#These lines establish the feed-forward part of the network used to choose actions
self.inputs1 = tf.placeholder(shape=[1, self.numStates],
dtype=tf.float32)
self.W = tf.Variable(
tf.random_uniform([self.numStates, self.numActions], 0, 0.01))
self.Qout = tf.matmul(self.inputs1, self.W)
self.predict = tf.argmax(self.Qout, 1)
#Below we obtain the loss by taking the sum of squares difference between the target and prediction Q values.
self.nextQ = tf.placeholder(shape=[1, self.numActions],
dtype=tf.float32)
self.loss = tf.reduce_sum(tf.square(self.nextQ - self.Qout))
self.trainer = tf.train.GradientDescentOptimizer(
learning_rate=learningRate)
self.updateModel = self.trainer.minimize(self.loss)
self.init = tf.global_variables_initializer(
) #tf.initialize_all_variables()
def __init__(self):
Responder.__init__(self)
self.batch_size = 1
self.gamma = 0.99
self.numRewardsForUpdate = 1
self.learningRate = 0.03
self.numHiddenNeurons = 1
self.createGraph()
def __init__(self):
Responder.__init__(self)
#we are using a trained model, no need to train further or to save or display results
self.recordRewards = False
self.plotResults = False
self.useTensorBoard = False
self.saveModels = False
self.loadModels = True
def __init__(self):
Responder.__init__(self)
def __init__(self):
Responder.__init__(self)
self.correctCharacter = [-1 for x in range(256)]
def __init__(self):
Responder.__init__(self)
self.characterIndex = -1
self.foundCharacter = False
self.specialCharacterOutputted = False
def __init__(self):
Responder.__init__(self)
self.characterIndex = -1
self.foundCharacter = False
def __init__(self):
Responder.__init__(self)
self.characterIndex = 0
def __init__(self):
Responder.__init__(self)
self.responses = [-1 for x in range(len(self.characters))]
self.counter = 0
self.steps = 0
def __init__(self):
Responder.__init__(self)
self.loadModels = True
def __init__(self):
Responder.__init__(self)
self.batch_size = 50
self.gamma=0.99
update_frequency = 5
self.createGraph()
def __init__(self): Responder.__init__(self) self.gamma = 0.99
