diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py
index aa8c64bacc3..ff6047310ff 100644
--- a/torchrl/envs/transforms/transforms.py
+++ b/torchrl/envs/transforms/transforms.py
@@ -795,6 +795,28 @@ def input_spec(self) -> TensorSpec:
             input_spec = self.__dict__.get("_input_spec", None)
         return input_spec
 
+    def rand_action(self, tensordict: Optional[TensorDictBase] = None) -> TensorDict:
+        if type(self.base_env).rand_action is not EnvBase.rand_action:
+            # TODO: this will fail if the transform modifies the input.
+            #  For instance, if an env overrides rand_action and we build a
+            #  env = PendulumEnv().append_transform(ActionDiscretizer(num_intervals=4))
+            #  env.rand_action will NOT have a discrete action!
+            #  Getting a discrete action would require coding the inverse transform of an action within
+            #  ActionDiscretizer (ie, float->int, not int->float).
+            #  We can loosely check that the action_spec isn't altered - that doesn't mean the action is
+            #  intact but it covers part of these alterations.
+            #
+            # The following check may be expensive to run and could be cached.
+            if self.full_action_spec != self.base_env.full_action_spec:
+                raise RuntimeError(
+                    f"The rand_action method from the base env {self.base_env.__class__.__name__} "
+                    "has been overwritten, but the transforms appended to the environment modify "
+                    "the action. To call the base env rand_action method, we should then invert the "
+                    "action transform, which is (in general) not doable."
+                )
+            return self.base_env.rand_action(tensordict)
+        return super().rand_action(tensordict)
+
     def _step(self, tensordict: TensorDictBase) -> TensorDictBase:
         # No need to clone here because inv does it already
         # tensordict = tensordict.clone(False)