DQN with RGB image as input

[ghorbelm]

Hello I try to do a DQN with a RGB image. Python code :

def build_QNetwork_RGB(n_actions, learning_rate, history_length, input_shape): """Builds a dueling DQN as a Keras model Arguments: n_actions: Number of possible action the agent can take learning_rate: Learning rate input_shape: Shape of the preprocessed frame the model sees history_length: Number of historical frames the agent can see Returns: A compiled Keras model """
model_input = Input(shape=(history_length, input_shape[0], input_shape[1], input_shape[2])) x = Lambda(lambda layer: layer / 255)(model_input) # normalize by 255 x = Conv2D(32, (8, 8), strides=4, kernel_initializer=VarianceScaling(scale=2.), activation='relu', use_bias=False)(x) x = Conv2D(64, (4, 4), strides=2, kernel_initializer=VarianceScaling(scale=2.), activation='relu', use_bias=False)(x) x = Conv2D(64, (3, 3), strides=1, kernel_initializer=VarianceScaling(scale=2.), activation='relu', use_bias=False)(x) x = Conv2D(1024, (7, 7), strides=1, kernel_initializer=VarianceScaling(scale=2.), activation='relu', use_bias=False)(x)

# Split into value and advantage streams
val_stream, adv_stream = Lambda(lambda w: tf.split(w, 2, 4))(x)  # custom splitting layer

val_stream = Flatten()(val_stream)
val = Dense(1, kernel_initializer=VarianceScaling(scale=2.))(val_stream)

adv_stream = Flatten()(adv_stream)
adv = Dense(n_actions, kernel_initializer=VarianceScaling(scale=2.))(adv_stream)

# Combine streams into Q-Values
reduce_mean = Lambda(lambda w: tf.reduce_mean(w, axis=1, keepdims=True))  # custom layer for reduce mean

q_vals = Add()([val, Subtract()([adv, reduce_mean(adv)])])

# Build model
model = Model(model_input, q_vals)
model.compile(Adam(learning_rate), loss=tf.keras.losses.Huber())    

model.summary()

return model**_ 

=========================================================== when I used this function to build network :

INPUT_SHAPE = (84, 84, 3) # Size of the preprocessed input frame. HISTORY_LENGTH = 5 Num_Actions = 4

BATCH_SIZE = 32 # Number of samples the agent learns from at once LEARNING_RATE = 0.00001

MAIN_DQN = build_QNetwork_RGB(Num_Actions, LEARNING_RATE, HISTORY_LENGTH, INPUT_SHAPE)

=========================================================== I got : Model: "model"

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Layer (type) Output Shape Param # Connected to

input_1 (InputLayer) [(None, 5, 84, 84, 3 0

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lambda (Lambda) (None, 5, 84, 84, 3) 0 input_1[0][0]

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conv2d (Conv2D) (None, 5, 20, 20, 32 6144 lambda[0][0]

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conv2d_1 (Conv2D) (None, 5, 9, 9, 64) 32768 conv2d[0][0]

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conv2d_2 (Conv2D) (None, 5, 7, 7, 64) 36864 conv2d_1[0][0]

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conv2d_3 (Conv2D) (None, 5, 1, 1, 1024 3211264 conv2d_2[0][0]

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lambda_1 (Lambda) [(None, 5, 1, 1, 512 0 conv2d_3[0][0]

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flatten_1 (Flatten) (None, 2560) 0 lambda_1[0][1]

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dense_1 (Dense) (None, 4) 10244 flatten_1[0][0]

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flatten (Flatten) (None, 2560) 0 lambda_1[0][0]

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lambda_2 (Lambda) (None, 1) 0 dense_1[0][0]

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dense (Dense) (None, 1) 2561 flatten[0][0]

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subtract (Subtract) (None, 4) 0 dense_1[0][0]
lambda_2[0][0]

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add (Add) (None, 4) 0 dense[0][0]
subtract[0][0]

Total params: 3,299,845 Trainable params: 3,299,845 Non-trainable params: 0

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I feel that this model is not well built. Do you have an idea how to correct it if this is the case?