diff --git a/sota-implementations/iql/utils.py b/sota-implementations/iql/utils.py
index 68acb9bfed4..168416d80da 100644
--- a/sota-implementations/iql/utils.py
+++ b/sota-implementations/iql/utils.py
@@ -388,6 +388,7 @@ def make_discrete_loss(loss_cfg, model):
         loss_function=loss_cfg.loss_function,
         temperature=loss_cfg.temperature,
         expectile=loss_cfg.expectile,
+        action_space="categorical",
     )
     loss_module.make_value_estimator(gamma=loss_cfg.gamma)
     target_net_updater = HardUpdate(
diff --git a/torchrl/data/utils.py b/torchrl/data/utils.py
index db2c8afca10..d43cbd7810d 100644
--- a/torchrl/data/utils.py
+++ b/torchrl/data/utils.py
@@ -307,7 +307,7 @@ def _process_action_space_spec(action_space, spec):
     return action_space, spec
 
 
-def _find_action_space(action_space):
+def _find_action_space(action_space) -> str:
     if isinstance(action_space, TensorSpec):
         if isinstance(action_space, Composite):
             if "action" in action_space.keys():
diff --git a/torchrl/objectives/iql.py b/torchrl/objectives/iql.py
index 71d1a22e17b..48667728071 100644
--- a/torchrl/objectives/iql.py
+++ b/torchrl/objectives/iql.py
@@ -782,7 +782,7 @@ def actor_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, dict]:
         # Min Q value
         td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False)
         td_q = self._vmap_qvalue_networkN0(td_q, self.target_qvalue_network_params)
-        state_action_value = td_q.get(self.tensor_keys.state_action_value)
+        state_action_value = td_q.get(self.tensor_keys.chosen_state_action_value)
         action = tensordict.get(self.tensor_keys.action)
         if self.action_space == "categorical":
             if action.shape != state_action_value.shape:
@@ -791,9 +791,11 @@ def actor_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, dict]:
             chosen_state_action_value = torch.gather(
                 state_action_value, -1, index=action
             ).squeeze(-1)
-        else:
+        elif self.action_space == "one_hot":
             action = action.to(torch.float)
             chosen_state_action_value = (state_action_value * action).sum(-1)
+        else:
+            raise RuntimeError(f"Unknown action space {self.action_space}.")
         min_Q, _ = torch.min(chosen_state_action_value, dim=0)
         if log_prob.shape != min_Q.shape:
             raise RuntimeError(
@@ -834,9 +836,11 @@ def value_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, dict]:
                 chosen_state_action_value = torch.gather(
                     state_action_value, -1, index=action
                 ).squeeze(-1)
-            else:
+            elif self.action_space == "one_hot":
                 action = action.to(torch.float)
                 chosen_state_action_value = (state_action_value * action).sum(-1)
+            else:
+                raise RuntimeError(f"Unknown action space {self.action_space}.")
             min_Q, _ = torch.min(chosen_state_action_value, dim=0)
         # state value
         td_copy = tensordict.select(*self.value_network.in_keys, strict=False)
@@ -867,9 +871,11 @@ def qvalue_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, dict]:
                 # unsqueeze the action if it lacks on trailing singleton dim
                 action = action.unsqueeze(-1)
             pred_val = torch.gather(state_action_value, -1, index=action).squeeze(-1)
-        else:
+        elif self.action_space == "one_hot":
             action = action.to(torch.float)
             pred_val = (state_action_value * action).sum(-1)
+        else:
+            raise RuntimeError(f"Unknown action space {self.action_space}.")
 
         td_error = (pred_val - target_value.expand_as(pred_val)).pow(2)
         loss_qval = distance_loss(