source/MXNetEnv/training/training_src/networks/qnetworks.py [196:211]:
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        self.sequence_length = sequence_length
        self.repeat_size = repeat_size
        mx.random.seed(seed)
        self.net = gluon.nn.HybridSequential()
        with self.net.name_scope():
            for i in range(number_of_conv_layers):
                self.net.add(gluon.nn.Conv2D(starting_channels*(i+1),
                                             kernel_size=kernel_size,
                                             strides=2,
                                             activation=activation_type))
             
            for _ in range(number_of_dense_layers):
                self.net.add(gluon.nn.Dense(number_of_hidden_states,
                                            activation=activation_type)) 

        self.net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
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source/MXNetEnv/training/training_src/networks/qnetworks.py [290:305]:
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        self.sequence_length = sequence_length
        self.repeat_size = repeat_size
        mx.random.seed(seed)
        self.net = gluon.nn.HybridSequential()
        with self.net.name_scope():
            for i in range(number_of_conv_layers):
                self.net.add(gluon.nn.Conv2D(starting_channels*(i+1),
                                             kernel_size=kernel_size,
                                             strides=2,
                                             activation=activation_type))
             
            for _ in range(number_of_dense_layers):
                self.net.add(gluon.nn.Dense(number_of_hidden_states,
                                            activation=activation_type)) 

        self.net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
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