Fix on GAIL Torch when using actions (#4407)

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/utils.py

         buffer["vector_obs"].append(curr_split_obs.vector_observations)
         buffer["next_vector_in"].append(next_split_obs.vector_observations)
         buffer["actions"].append(action)
-        buffer["done"].append(np.zeros(1, dtype=np.float32))
+    buffer["done"] = np.zeros(number, dtype=np.float32)
     return buffer

ml-agents/mlagents/trainers/torch/components/reward_providers/gail_reward_provider.py

         encoder_input = self.get_state_encoding(mini_batch)
         if self._settings.use_actions:
             actions = self.get_action_input(mini_batch)
-            dones = torch.as_tensor(mini_batch["done"], dtype=torch.float)
+            dones = torch.as_tensor(mini_batch["done"], dtype=torch.float).unsqueeze(1)
             encoder_input = torch.cat([encoder_input, actions, dones], dim=1)
         hidden = self.encoder(encoder_input)
         z_mu: Optional[torch.Tensor] = None
             policy_action = self.get_action_input(policy_batch)
             expert_action = self.get_action_input(policy_batch)
             action_epsilon = torch.rand(policy_action.shape)
-            policy_dones = torch.as_tensor(policy_batch["done"], dtype=torch.float)
-            expert_dones = torch.as_tensor(expert_batch["done"], dtype=torch.float)
+            policy_dones = torch.as_tensor(
+                policy_batch["done"], dtype=torch.float
+            ).unsqueeze(1)
+            expert_dones = torch.as_tensor(
+                expert_batch["done"], dtype=torch.float
+            ).unsqueeze(1)
             dones_epsilon = torch.rand(policy_dones.shape)
             encoder_input = torch.cat(
                 [