def create_model(self, model_info):
"""Create Deep-Q CNN network."""
state = Input(shape=self.state_dim, dtype="uint8")
state1 = Lambda(lambda x: K.cast(x, dtype='float32') / 255.)(state)
convlayer = Conv2D(32, (8, 8),
strides=(4, 4),
activation='relu',
padding='valid')(state1)
convlayer = Conv2D(64, (4, 4),
strides=(2, 2),
activation='relu',
padding='valid')(convlayer)
convlayer = Conv2D(64, (3, 3),
strides=(1, 1),
activation='relu',
padding='valid')(convlayer)
flattenlayer = Flatten()(convlayer)
denselayer = Dense(256, activation='relu')(flattenlayer)
value = Dense(self.action_dim, activation='linear')(denselayer)
model = Model(inputs=state, outputs=value)
adam = Adam(lr=self.learning_rate, clipnorm=10.)
model.compile(loss='mse', optimizer=adam)
if model_info.get("summary"):
model.summary()
self.infer_state = tf.placeholder(tf.uint8,
name="infer_input",
shape=(None, ) +
tuple(self.state_dim))
self.infer_v = model(self.infer_state)
self.actor_var = TFVariables([self.infer_v], self.sess)
self.sess.run(tf.initialize_all_variables())
return model
def layer_function(x):
"""Normalize data."""
return K.cast(x, dtype='float32') / 255.
