approximators.py 文件源码

def _initialize_network(self, img_input_shape, misc_len, output_size, img_input, misc_input=None, **kwargs):
        input_layers = []
        inputs = [img_input]
        # weights_init = lasagne.init.GlorotUniform("relu")
        weights_init = lasagne.init.HeNormal("relu")

        network = ls.InputLayer(shape=img_input_shape, input_var=img_input)
        input_layers.append(network)
        network = ls.Conv2DLayer(network, num_filters=32, filter_size=8, nonlinearity=rectify, W=weights_init,
                                 b=lasagne.init.Constant(.1), stride=4)
        network = ls.Conv2DLayer(network, num_filters=64, filter_size=4, nonlinearity=rectify, W=weights_init,
                                 b=lasagne.init.Constant(.1), stride=2)
        network = ls.Conv2DLayer(network, num_filters=64, filter_size=3, nonlinearity=rectify, W=weights_init,
                                 b=lasagne.init.Constant(.1), stride=1)

        if self.misc_state_included:
            inputs.append(misc_input)
            network = ls.FlattenLayer(network)
            misc_input_layer = ls.InputLayer(shape=(None, misc_len), input_var=misc_input)
            input_layers.append(misc_input_layer)
            if "additional_misc_layer" in kwargs:
                misc_input_layer = ls.DenseLayer(misc_input_layer, int(kwargs["additional_misc_layer"]),
                                                 nonlinearity=rectify,
                                                 W=weights_init, b=lasagne.init.Constant(0.1))
            network = ls.ConcatLayer([network, misc_input_layer])

        # Duelling here

        advanteges_branch = ls.DenseLayer(network, 256, nonlinearity=rectify,
                                          W=weights_init, b=lasagne.init.Constant(.1))
        advanteges_branch = ls.DenseLayer(advanteges_branch, output_size, nonlinearity=None,
                                          b=lasagne.init.Constant(.1))

        state_value_branch = ls.DenseLayer(network, 256, nonlinearity=rectify,
                                           W=weights_init, b=lasagne.init.Constant(.1))
        state_value_branch = ls.DenseLayer(state_value_branch, 1, nonlinearity=None,
                                           b=lasagne.init.Constant(.1))

        network = DuellingMergeLayer([advanteges_branch, state_value_branch])
        return network, input_layers, inputs