Advanced Deep Learning with Keras

Policy Gradient Methods
self.encoder = Model(inputs, feature, name='encoder')
self.encoder.summary()
plot_model(self.encoder, to_file='encoder.png',
show_shapes=True)
# build the decoder model
feature_inputs = Input(shape=(feature_size,),
name='decoder_input')
x = Dense(128, activation='relu')(feature_inputs)
x = Dense(256, activation='relu')(x)
outputs = Dense(self.state_dim, activation='linear')(x)
# instantiate decoder model
self.decoder = Model(feature_inputs, outputs, name='decoder')
self.decoder.summary()
plot_model(self.decoder, to_file='decoder.png',
show_shapes=True)
# autoencoder = encoder + decoder
# instantiate autoencoder model
self.autoencoder = Model(inputs,
self.decoder(self.encoder(inputs)), name='autoencoder')
self.autoencoder.summary()
plot_model(self.autoencoder, to_file='autoencoder.png',
show_shapes=True)
# Mean Square Error (MSE) loss function, Adam optimizer
self.autoencoder.compile(loss='mse', optimizer='adam')
# training the autoencoder using randomly sampled
# states from the environment
def train_autoencoder(self, x_train, x_test):
# train the autoencoder
batch_size = 32
self.autoencoder.fit(x_train,
x_train,
validation_data=(x_test, x_test),
epochs=10,
batch_size=batch_size)
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