source/MXNetEnv/training/training_src/networks/qnetworks.py [90:102]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        self.predict.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
   
        if self.sequence_length > 1:
            self.gru = gluon.rnn.GRU(number_of_hidden_states, num_layers=1,
                                     layout='NTC')
            self.gru.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
   
    def hybrid_forward(self, F, state_sequence, snake_id_sequence, turn_count_sequence, snake_health_sequence):
        """Build a network that maps states -> action values."""
        resized_state_sequence = state_sequence.repeat(
                axis=3, repeats=self.repeat_size).repeat(
                    axis=4,repeats=self.repeat_size)
        new_state_sequence = resized_state_sequence.reshape((-3, -2))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



source/MXNetEnv/training/training_src/networks/qnetworks.py [223:236]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        self.predict.collect_params().initialize(mx.init.Xavier(), ctx=ctx)

        if self.sequence_length > 1:
            self.gru = gluon.rnn.GRU(number_of_hidden_states, num_layers=1,
                                     layout='NTC')
            self.gru.collect_params().initialize(mx.init.Xavier(), ctx=ctx)
   
    def hybrid_forward(self, F, state_sequence, snake_id_sequence, turn_count_sequence, snake_health_sequence):
        """Build a network that maps states -> action values."""

        resized_state_sequence = state_sequence.repeat(
                axis=3, repeats=self.repeat_size).repeat(
                    axis=4,repeats=self.repeat_size)
        new_state_sequence = resized_state_sequence.reshape((-3, -2))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -