agent_LSTM.py 文件源码

def _build_model(self):
        # Deep Conv Neural Net for Deep-Q learning Model
        model = Sequential()
        model.add(Conv1D(128, 3, input_shape=(19,48)))
        model.add(Activation('relu'))
        model.add(MaxPooling1D(pool_size=2))

        model.add(Conv1D(64, 3))
        model.add(Activation('relu'))
        model.add(MaxPooling1D(pool_size=2))

        model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
        model.add(Dense(64))
        model.add(Activation('relu'))
        model.add(Dropout(0.5))
        model.add(Dense(self.action_size))
        model.add(Activation('sigmoid'))

        model.compile(loss=self._huber_loss,
                      optimizer=Adam(lr=self.learning_rate))
        #model.compile(loss='binary_crossentropy',
        #              optimizer='rmsprop',
        #              metrics=['accuracy'])

        return model