diff --git a/test/test_transforms.py b/test/test_transforms.py
index ed953e7ce45..c63f6f69828 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -1275,6 +1275,22 @@ def test_noop_reset_env(self, random, device, compose):
         else:
             assert transformed_env.step_count == 30
 
+    @pytest.mark.parametrize("random", [True, False])
+    @pytest.mark.parametrize("compose", [True, False])
+    @pytest.mark.parametrize("device", get_available_devices())
+    def test_noop_reset_env_error(self, random, device, compose):
+        torch.manual_seed(0)
+        env = SerialEnv(3, lambda: ContinuousActionVecMockEnv())
+        env.set_seed(100)
+        noop_reset_env = NoopResetEnv(random=random)
+        transformed_env = TransformedEnv(env)
+        transformed_env.append_transform(noop_reset_env)
+        with pytest.raises(
+            ValueError,
+            match="there is more than one done state in the parent environment",
+        ):
+            transformed_env.reset()
+
     @pytest.mark.parametrize(
         "default_keys", [["action"], ["action", "monkeys jumping on the bed"]]
     )
diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py
index b9bf02fcdc5..dbd2c118893 100644
--- a/torchrl/envs/common.py
+++ b/torchrl/envs/common.py
@@ -399,9 +399,11 @@ def reset(
                 f"env._reset returned an object of type {type(tensordict_reset)} but a TensorDict was expected."
             )
 
-        self.is_done = tensordict_reset.get(
+        self.is_done = tensordict_reset.set_default(
             "done",
-            torch.zeros(self.batch_size, dtype=torch.bool, device=self.device),
+            torch.zeros(
+                *tensordict_reset.batch_size, 1, dtype=torch.bool, device=self.device
+            ),
         )
         if self.is_done:
             raise RuntimeError(
diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py
index b14ae8b0508..62c0d6b22f4 100644
--- a/torchrl/envs/transforms/transforms.py
+++ b/torchrl/envs/transforms/transforms.py
@@ -1960,40 +1960,48 @@ def base_env(self):
 
     def reset(self, tensordict: TensorDictBase) -> TensorDictBase:
         """Do no-op action for a number of steps in [1, noop_max]."""
+        td_reset = tensordict.clone(False)
+        tensordict = tensordict.clone(False)
+        # check that there is a single done state -- behaviour is undefined for multiple dones
         parent = self.parent
-        # keys = tensordict.keys()
+        if tensordict.get("done").numel() > 1:
+            raise ValueError(
+                "there is more than one done state in the parent environment. "
+                "NoopResetEnv is designed to work on single env instances, as partial reset "
+                "is currently not supported. If you feel like this is a missing feature, submit "
+                "an issue on TorchRL github repo. "
+                "In case you are trying to use NoopResetEnv over a batch of environments, know "
+                "that you can have a transformed batch of transformed envs, such as: "
+                "`TransformedEnv(ParallelEnv(3, lambda: TransformedEnv(MyEnv(), NoopResetEnv(3))), OtherTransform())`."
+            )
         noops = (
             self.noops if not self.random else torch.randint(self.noops, (1,)).item()
         )
-        i = 0
         trial = 0
 
-        while i < noops:
-            i += 1
-            tensordict = parent.rand_step(tensordict)
-            tensordict = step_mdp(tensordict)
-            if parent.is_done:
-                parent.reset()
-                i = 0
-                trial += 1
-                if trial > _MAX_NOOPS_TRIALS:
-                    tensordict = parent.reset(tensordict)
-                    tensordict = parent.rand_step(tensordict)
+        while True:
+            i = 0
+            while i < noops:
+                i += 1
+                tensordict = parent.rand_step(tensordict)
+                tensordict = step_mdp(tensordict, exclude_done=False)
+                if tensordict.get("done"):
+                    tensordict = parent.reset(td_reset.clone(False))
                     break
-        if parent.is_done:
-            raise RuntimeError("NoopResetEnv concluded with done environment")
-        # td = step_mdp(
-        #     tensordict, exclude_done=False, exclude_reward=True, exclude_action=True
-        # )
-
-        # for k in keys:
-        #     if k not in td.keys():
-        #         td.set(k, tensordict.get(k))
-
-        # # replace the next_ prefix
-        # for out_key in parent.observation_spec:
-        #     td.rename_key(out_key[5:], out_key)
+            else:
+                break
+
+            trial += 1
+            if trial > _MAX_NOOPS_TRIALS:
+                tensordict = parent.rand_step(tensordict)
+                if tensordict.get("done"):
+                    raise RuntimeError(
+                        f"parent is still done after a single random step (i={i})."
+                    )
+                break
 
+        if tensordict.get("done"):
+            raise RuntimeError("NoopResetEnv concluded with done environment")
         return tensordict
 
     def __repr__(self) -> str: