Support multiple goals in networkbody

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

         memories: Optional[torch.Tensor] = None,
         sequence_length: int = 1,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        encodes = []
-        goal_signal = None
+        obs_encodes = []
+        goal_encodes = []
         for idx, processor in enumerate(self.processors):
             obs_input = inputs[idx]
             processed_obs = processor(obs_input)
             ):
-                encodes.append(processed_obs)
+                obs_encodes.append(processed_obs)
-                if goal_signal is not None:
-                    raise Exception("TODO : Cannot currently handle more than one goal")
-                goal_signal = processed_obs
+                goal_encodes.append(processed_obs)
+            else:
+                raise Exception("TODO : Something other than a goal or observation was passed to the agent.")
-        if len(encodes) == 0:
+        if len(obs_encodes) == 0:
-            inputs = torch.cat(encodes + [actions], dim=-1)
+            obs_inputs = torch.cat(obs_encodes + [actions], dim=-1)
-            inputs = torch.cat(encodes, dim=-1)
+            obs_inputs = torch.cat(obs_encodes, dim=-1)
-        if goal_signal is None:
-            encoding = self.linear_encoder(inputs)
+        if len(goal_encodes) == 0:
+            encoding = self.linear_encoder(obs_inputs)
-            encoding = self.linear_encoder(inputs, goal_signal)
+            goal_inputs = torch.cat(goal_encodes, dim=-1)
+            encoding = self.linear_encoder(obs_inputs, goal_inputs)
 
         if self.use_lstm:
             # Resize to (batch, sequence length, encoding size)