diff --git a/test/test_env.py b/test/test_env.py
index a36c88b6282..4661211f863 100644
--- a/test/test_env.py
+++ b/test/test_env.py
@@ -508,7 +508,7 @@ def test_parallel_env(
             td = TensorDict(
                 source={"action": env0.action_spec.rand((N - 1,))}, batch_size=[N - 1]
             )
-            td1 = env_parallel.step(td)
+            _ = env_parallel.step(td)
 
         td_reset = TensorDict(
             source={"reset_workers": torch.zeros(N, dtype=torch.bool).bernoulli_()},
@@ -592,7 +592,7 @@ def test_parallel_env_with_policy(
             td = TensorDict(
                 source={"action": env0.action_spec.rand((N - 1,))}, batch_size=[N - 1]
             )
-            td1 = env_parallel.step(td)
+            _ = env_parallel.step(td)
 
         td_reset = TensorDict(
             source={"reset_workers": torch.zeros(N, dtype=torch.bool).bernoulli_()},
@@ -710,7 +710,6 @@ def test_parallel_env_cast(
         transformed_out,
         device,
         open_before,
-        T=10,
         N=3,
     ):
         # tests casting to device
diff --git a/torchrl/envs/vec_env.py b/torchrl/envs/vec_env.py
index a74a70c3d86..af44cc533e5 100644
--- a/torchrl/envs/vec_env.py
+++ b/torchrl/envs/vec_env.py
@@ -18,7 +18,6 @@
 from tensordict import TensorDict
 from tensordict.tensordict import LazyStackedTensorDict, TensorDictBase
 from torch import multiprocessing as mp
-
 from torchrl._utils import _check_for_faulty_process
 from torchrl.data import CompositeSpec, TensorSpec
 from torchrl.data.utils import CloudpickleWrapper, DEVICE_TYPING
@@ -781,10 +780,9 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase:
         for i in range(self.num_workers):
             msg, data = self.parent_channels[i].recv()
             if msg != "step_result":
-                if msg != "done":
-                    raise RuntimeError(
-                        f"Expected 'done' but received {msg} from worker {i}"
-                    )
+                raise RuntimeError(
+                    f"Expected 'step_result' but received {msg} from worker {i}"
+                )
             # data is the set of updated keys
             keys = keys.union(data)
         # We must pass a clone of the tensordict, as the values of this tensordict
@@ -1004,13 +1002,18 @@ def _run_worker_pipe_shared_mem(
                 raise RuntimeError("call 'init' before resetting")
             # _td = tensordict.select("observation").to(env.device).clone()
             _td = env._reset(**reset_kwargs)
+            done = _td.get("done", None)
+            if done is None:
+                _td["done"] = done = torch.zeros(
+                    *_td.batch_size, 1, dtype=torch.bool, device=env.device
+                )
             if reset_keys is None:
                 reset_keys = set(_td.keys())
             if pin_memory:
                 _td.pin_memory()
             tensordict.update_(_td)
             child_pipe.send(("reset_obs", reset_keys))
-            if _td.get("done").any():
+            if done.any():
                 raise RuntimeError(f"{env.__class__.__name__} is done after reset")
 
         elif cmd == "step":
@@ -1029,10 +1032,7 @@ def _run_worker_pipe_shared_mem(
             if pin_memory:
                 _td.pin_memory()
             tensordict.update_(_td.select(*step_keys, strict=False))
-            if _td.get("done"):
-                msg = "done"
-            else:
-                msg = "step_result"
+            msg = "step_result"
             data = (msg, step_keys)
             child_pipe.send(data)