Q_Learning_Agent.py 文件源码

def create_neural_network_rnn(self):
        """
        Create the Neural Network Model

        :return: Keras Modelh
        """

        model = Sequential()

        # we start off with an efficient embedding layer which maps
        # our vocab indices into embedding_dims dimensions
        model.add(Embedding(12,  # Number of Features from State Space
                            300,  # Vector Size
                            input_length=self.input_dim))

        # we add a Convolution1D, which will learn nb_filter
        # word group filters of size filter_length:
        model.add(Convolution1D(nb_filter=self.nb_filter,
                                filter_length=self.filter_length,
                                border_mode='valid',
                                activation='relu',
                                subsample_length=1))

        # we use standard max pooling (halving the output of the previous
        # layer):
        model.add(MaxPooling1D(pool_length=self.pool_length))
        model.add(Dropout(self.dropout))

        # We flatten the output of the conv layer,
        # so that we can add a vanilla dense layer:
        model.add(Flatten())

        # We add a vanilla hidden layer:
        model.add(Dense(self.neurons))
        model.add(Dropout(self.dropout))
        model.add(Activation('relu'))

        # We project onto a single unit output layer, and squash it with a
        # sigmoid:
        model.add(Dense(len(self.actions)))
        model.add(Activation('linear'))

        model.compile(loss='mse',
                      optimizer=Adadelta(lr=0.00025))

        print(model.summary())

        return model