def __init__(<EOL>self,<EOL>size,<EOL>bias=False,<EOL>activation='<STR_LIT:none>',<EOL>l2_regularization=<NUM_LIT:0.0>,<EOL>l1_regularization=<NUM_LIT:0.0>,<EOL>output=None,<EOL>named_tensors=None,<EOL>scope='<STR_LIT>',<EOL>summary_labels=()<EOL>):
<EOL>self.expectation_layer = Linear(<EOL>size=<NUM_LIT:1>, bias=bias,<EOL>l2_regularization=l2_regularization,<EOL>l1_regularization=l1_regularization,<EOL>summary_labels=summary_labels,<EOL>)<EOL>self.advantage_layer = Linear(<EOL>size=size,<EOL>bias=bias,<EOL>l2_regularization=l2_regularization,<EOL>l1_regularization=l1_regularization,<EOL>summary_labels=summary_labels,<EOL>)<EOL>self.output = output<EOL>self.nonlinearity = Nonlinearity(summary_labels=summary_labels, **util.prepare_kwargs(activation))<EOL>super(Dueling, self).__init__(named_tensors=named_tensors, scope=scope, summary_labels=summary_labels)<EOL>
Dueling layer.

[Dueling Networks] (https://arxiv.org/pdf/1511.06581.pdf)
Implement Y = Expectation[x] + (Advantage[x] - Mean(Advantage[x]))

Args:
    size: Layer size.
    bias: If true, bias is added.
    activation: Type of nonlinearity, or dict with name & arguments
    l2_regularization: L2 regularization weight.
    l1_regularization: L1 regularization weight.
    output: None or tuple of output names for ('expectation','advantage','mean_advantage')