diff --git a/benchmarl/experiment/experiment.py b/benchmarl/experiment/experiment.py
index 212e3c99..223fc9a4 100644
--- a/benchmarl/experiment/experiment.py
+++ b/benchmarl/experiment/experiment.py
@@ -442,24 +442,30 @@ def _setup_task(self):
         transforms_training = transforms_env + [
             self.task.get_reward_sum_transform(test_env)
         ]
-
         transforms_env = Compose(*transforms_env)
         transforms_training = Compose(*transforms_training)
 
-        self.observation_spec = self.task.observation_spec(test_env)
-        self.info_spec = self.task.info_spec(test_env)
-        self.state_spec = self.task.state_spec(test_env)
-        self.action_mask_spec = self.task.action_mask_spec(test_env)
-        self.action_spec = self.task.action_spec(test_env)
-        self.group_map = self.task.group_map(test_env)
+        # Initialize test env
+        self.test_env = TransformedEnv(test_env, transforms_env.clone()).to(
+            self.config.sampling_device
+        )
+
+        self.observation_spec = self.task.observation_spec(self.test_env)
+        self.info_spec = self.task.info_spec(self.test_env)
+        self.state_spec = self.task.state_spec(self.test_env)
+        self.action_mask_spec = self.task.action_mask_spec(self.test_env)
+        self.action_spec = self.task.action_spec(self.test_env)
+        self.group_map = self.task.group_map(self.test_env)
         self.train_group_map = copy.deepcopy(self.group_map)
-        self.max_steps = self.task.max_steps(test_env)
+        self.max_steps = self.task.max_steps(self.test_env)
 
+        # Add rnn transforms here so they do not show in the benchmarl specs
         if self.model_config.is_rnn:
-            test_env = self._add_rnn_transforms(lambda: test_env)()
+            self.test_env = self._add_rnn_transforms(lambda: self.test_env)()
             env_func = self._add_rnn_transforms(env_func)
 
-        if test_env.batch_size == ():
+        # Initialize train env
+        if self.test_env.batch_size == ():
             # If the environment is not vectorized, we simulate vectorization using parallel or serial environments
             env_class = (
                 SerialEnv if not self.config.parallel_collection else ParallelEnv
@@ -469,14 +475,11 @@ def _setup_task(self):
                 transforms_training.clone(),
             )
         else:
+            # Otherwise it is already vectorized
             self.env_func = lambda: TransformedEnv(
                 env_func(), transforms_training.clone()
             )
 
-        self.test_env = TransformedEnv(test_env, transforms_env.clone()).to(
-            self.config.sampling_device
-        )
-
     def _setup_algorithm(self):
         self.algorithm = self.algorithm_config.get_algorithm(experiment=self)