Add goal to hyper input

ml-agents/mlagents/trainers/torch/layers.py

     ):
         super().__init__()
         self.layers = []
-        prev_size = input_size
+        prev_size = input_size + goal_size
         for i in range(num_layers):
             if i < num_layers - num_hyper_layers:
                 self.layers.append(
     def forward(
         self, input_tensor: torch.Tensor, goal_tensor: torch.Tensor
     ) -> torch.Tensor:
-        activation = input_tensor
+        activation = torch.cat([input_tensor, goal_tensor], dim=-1)
         for layer in self.layers:
             if isinstance(layer, HyperNetwork):
                 activation = layer(activation, goal_tensor)

ml-agents/mlagents/trainers/torch/networks.py

         encoded_act_size: int = 0,
     ):
         super().__init__()
-        self.conditioning_mode = ConditioningMode.SOFT
+        self.conditioning_mode = ConditioningMode.HYPER
         self.normalize = network_settings.normalize
         self.use_lstm = network_settings.memory is not None
         self.h_size = network_settings.hidden_units