dqn.py 文件源码

def createModel(self):
        input_shape = (self.img_channels, self.img_rows, self.img_cols)
        if K.image_dim_ordering() == 'tf':
            input_shape = ( self.img_rows, self.img_cols, self.img_channels)

        model = Sequential()
        model.add(Convolution2D(16, 3, 3,border_mode='same', input_shape = input_shape))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Convolution2D(32, 3, 3, border_mode='same'))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))


        model.add(Convolution2D(64, 3, 3, border_mode='same'))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))
        model.add(Flatten())


        model.add(Dense(256))
        model.add(Activation('relu'))
        model.add(Dropout(0.25))
        model.add(Dense(256))
        model.add(Activation('relu'))
        model.add(Dropout(0.25))
        model.add(Dense(self.output_size,activation='linear'))
        model.compile(Adam(lr=self.learningRate), 'MSE')
        model.summary()


        return model