diff --git a/test/test_cost.py b/test/test_cost.py
index 8bbb7edce05..c8e45624580 100644
--- a/test/test_cost.py
+++ b/test/test_cost.py
@@ -40,6 +40,7 @@
 from tensordict.nn.utils import Buffer
 from tensordict.utils import unravel_key
 from torch import autograd, nn
+from torchrl._utils import _standardize
 from torchrl.data import Bounded, Categorical, Composite, MultiOneHot, OneHot, Unbounded
 from torchrl.data.postprocs.postprocs import MultiStep
 from torchrl.envs.model_based.dreamer import DreamerEnv
@@ -16044,6 +16045,18 @@ def _composite_log_prob(self):
         yield
         setter.unset()
 
+    def test_standardization(self):
+        t = torch.arange(3 * 4 * 5 * 6, dtype=torch.float32).view(3, 4, 5, 6)
+        std_t0 = _standardize(t, exclude_dims=(1, 3))
+        std_t1 = (t - t.mean((0, 2), keepdim=True)) / t.std((0, 2), keepdim=True).clamp(
+            1 - 6
+        )
+        torch.testing.assert_close(std_t0, std_t1)
+        std_t = _standardize(t, (), -1, 2)
+        torch.testing.assert_close(std_t, (t + 1) / 2)
+        std_t = _standardize(t, ())
+        torch.testing.assert_close(std_t, (t - t.mean()) / t.std())
+
     @pytest.mark.parametrize("B", [None, (1, ), (4, ), (2, 2, ), (1, 2, 8, )])  # fmt: skip
     @pytest.mark.parametrize("T", [1, 10])
     @pytest.mark.parametrize("device", get_default_devices())
diff --git a/torchrl/_utils.py b/torchrl/_utils.py
index 6a2f80aeffb..f999fa96c1d 100644
--- a/torchrl/_utils.py
+++ b/torchrl/_utils.py
@@ -24,7 +24,7 @@
 from distutils.util import strtobool
 from functools import wraps
 from importlib import import_module
-from typing import Any, Callable, cast, Dict, TypeVar, Union
+from typing import Any, Callable, cast, Dict, Tuple, TypeVar, Union
 
 import numpy as np
 import torch
@@ -32,7 +32,7 @@
 from tensordict import unravel_key
 
 from tensordict.utils import NestedKey
-from torch import multiprocessing as mp
+from torch import multiprocessing as mp, Tensor
 
 try:
     from torch.compiler import is_compiling
@@ -872,6 +872,70 @@ def set_mode(self, type: Any | None) -> None:
             self._mode = type
 
 
+def _standardize(
+    input: Tensor,
+    exclude_dims: Tuple[int] = (),
+    mean: Tensor | None = None,
+    std: Tensor | None = None,
+    eps: float | None = None,
+):
+    """Standardizes the input tensor with the possibility of excluding specific dims from the statistics.
+
+    Useful when processing multi-agent data to keep the agent dimensions independent.
+
+    Args:
+        input (Tensor): the input tensor to be standardized.
+        exclude_dims (Tuple[int]): dimensions to exclude from the statistics, can be negative. Default: ().
+        mean (Tensor): a mean to be used for standardization. Must be of shape broadcastable to input. Default: None.
+        std (Tensor): a standard deviation to be used for standardization. Must be of shape broadcastable to input. Default: None.
+        eps (float): epsilon to be used for numerical stability. Default: float32 resolution.
+
+    """
+    if eps is None:
+        if input.dtype.is_floating_point:
+            eps = torch.finfo(torch.float).resolution
+        else:
+            eps = 1e-6
+
+    len_exclude_dims = len(exclude_dims)
+    if not len_exclude_dims:
+        if mean is None:
+            mean = input.mean()
+        else:
+            # Assume dtypes are compatible
+            mean = torch.as_tensor(mean, device=input.device)
+        if std is None:
+            std = input.std()
+        else:
+            # Assume dtypes are compatible
+            std = torch.as_tensor(std, device=input.device)
+        return (input - mean) / std.clamp_min(eps)
+
+    input_shape = input.shape
+    exclude_dims = [
+        d if d >= 0 else d + len(input_shape) for d in exclude_dims
+    ]  # Make negative dims positive
+
+    if len(set(exclude_dims)) != len_exclude_dims:
+        raise ValueError("Exclude dims has repeating elements")
+    if any(dim < 0 or dim >= len(input_shape) for dim in exclude_dims):
+        raise ValueError(
+            f"exclude_dims={exclude_dims} provided outside bounds for input of shape={input_shape}"
+        )
+    if len_exclude_dims == len(input_shape):
+        warnings.warn(
+            "_standardize called but all dims were excluded from the statistics, returning unprocessed input"
+        )
+        return input
+
+    included_dims = tuple(d for d in range(len(input_shape)) if d not in exclude_dims)
+    if mean is None:
+        mean = torch.mean(input, keepdim=True, dim=included_dims)
+    if std is None:
+        std = torch.std(input, keepdim=True, dim=included_dims)
+    return (input - mean) / std.clamp_min(eps)
+
+
 @wraps(torch.compile)
 def compile_with_warmup(*args, warmup: int = 1, **kwargs):
     """Compile a model with warm-up.
diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py
index 5b6763f6910..2a70f70a3e2 100644
--- a/torchrl/envs/batched_envs.py
+++ b/torchrl/envs/batched_envs.py
@@ -1217,6 +1217,10 @@ class ParallelEnv(BatchedEnvBase, metaclass=_PEnvMeta):
     __doc__ += BatchedEnvBase.__doc__
     __doc__ += """
 
+    .. note:: ParallelEnv will timeout after one of the worker is idle for a determinate amount of time.
+        This can be controlled via the BATCHED_PIPE_TIMEOUT environment variable, which in turn modifies
+        the torchrl._utils.BATCHED_PIPE_TIMEOUT integer. The default timeout value is 10000 seconds.
+
     .. warning::
       TorchRL's ParallelEnv is quite stringent when it comes to env specs, since
       these are used to build shared memory buffers for inter-process communication.
@@ -1353,7 +1357,10 @@ class ParallelEnv(BatchedEnvBase, metaclass=_PEnvMeta):
     """
 
     def _start_workers(self) -> None:
+        import torchrl
+
         self._timeout = 10.0
+        self.BATCHED_PIPE_TIMEOUT = torchrl._utils.BATCHED_PIPE_TIMEOUT
 
         from torchrl.envs.env_creator import EnvCreator
 
@@ -1606,7 +1613,7 @@ def step_and_maybe_reset(
 
         for i in workers_range:
             event = self._events[i]
-            event.wait(self._timeout)
+            event.wait(self.BATCHED_PIPE_TIMEOUT)
             event.clear()
 
         if self._non_tensor_keys:
@@ -1796,7 +1803,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase:
 
         for i in workers_range:
             event = self._events[i]
-            event.wait(self._timeout)
+            event.wait(self.BATCHED_PIPE_TIMEOUT)
             event.clear()
 
         if self._non_tensor_keys:
@@ -1965,7 +1972,7 @@ def tentative_update(val, other):
 
         for i, _ in outs:
             event = self._events[i]
-            event.wait(self._timeout)
+            event.wait(self.BATCHED_PIPE_TIMEOUT)
             event.clear()
 
         workers_nontensor = []
@@ -2023,7 +2030,7 @@ def _shutdown_workers(self) -> None:
             for channel in self.parent_channels:
                 channel.close()
             for proc in self._workers:
-                proc.join(timeout=1.0)
+                proc.join(timeout=self._timeout)
         finally:
             for proc in self._workers:
                 if proc.is_alive():
diff --git a/torchrl/modules/tensordict_module/probabilistic.py b/torchrl/modules/tensordict_module/probabilistic.py
index 4e8c2c3b87b..392b7291df9 100644
--- a/torchrl/modules/tensordict_module/probabilistic.py
+++ b/torchrl/modules/tensordict_module/probabilistic.py
@@ -2,6 +2,7 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
+from __future__ import annotations
 
 import warnings
 from typing import Dict, List, Optional, Type, Union
diff --git a/torchrl/objectives/a2c.py b/torchrl/objectives/a2c.py
index e54f71bba7d..7701c1a662f 100644
--- a/torchrl/objectives/a2c.py
+++ b/torchrl/objectives/a2c.py
@@ -257,6 +257,7 @@ def __post_init__(self):
                     self.sample_log_prob = "action_log_prob"
 
     default_keys = _AcceptedKeys
+    tensor_keys: _AcceptedKeys
     default_value_estimator: ValueEstimators = ValueEstimators.GAE
 
     actor_network: TensorDictModule
diff --git a/torchrl/objectives/common.py b/torchrl/objectives/common.py
index 5a6893412a4..ab5a564abcf 100644
--- a/torchrl/objectives/common.py
+++ b/torchrl/objectives/common.py
@@ -128,6 +128,7 @@ class _AcceptedKeys:
 
         pass
 
+    tensor_keys: _AcceptedKeys
     _vmap_randomness = None
     default_value_estimator: ValueEstimators = None
 
diff --git a/torchrl/objectives/cql.py b/torchrl/objectives/cql.py
index df6ebb18571..f10dcab1e0e 100644
--- a/torchrl/objectives/cql.py
+++ b/torchrl/objectives/cql.py
@@ -260,6 +260,7 @@ class _AcceptedKeys:
         done: NestedKey = "done"
         terminated: NestedKey = "terminated"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
 
@@ -1024,6 +1025,7 @@ class _AcceptedKeys:
         terminated: NestedKey = "terminated"
         pred_val: NestedKey = "pred_val"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
     out_keys = [
diff --git a/torchrl/objectives/crossq.py b/torchrl/objectives/crossq.py
index 162bce09c0d..d7dc5976308 100644
--- a/torchrl/objectives/crossq.py
+++ b/torchrl/objectives/crossq.py
@@ -242,6 +242,7 @@ class _AcceptedKeys:
         terminated: NestedKey = "terminated"
         log_prob: NestedKey = "_log_prob"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
 
diff --git a/torchrl/objectives/ddpg.py b/torchrl/objectives/ddpg.py
index b9617d8d6b3..fde8df0a93a 100644
--- a/torchrl/objectives/ddpg.py
+++ b/torchrl/objectives/ddpg.py
@@ -173,6 +173,7 @@ class _AcceptedKeys:
         done: NestedKey = "done"
         terminated: NestedKey = "terminated"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator: ValueEstimators = ValueEstimators.TD0
     out_keys = [
diff --git a/torchrl/objectives/decision_transformer.py b/torchrl/objectives/decision_transformer.py
index e7df82f21a9..acb7156ad6e 100644
--- a/torchrl/objectives/decision_transformer.py
+++ b/torchrl/objectives/decision_transformer.py
@@ -70,6 +70,7 @@ class _AcceptedKeys:
         # the "action" output from the model
         action_pred: NestedKey = "action"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
 
     actor_network: TensorDictModule
@@ -280,6 +281,7 @@ class _AcceptedKeys:
         # the "action" output from the model
         action_pred: NestedKey = "action"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
 
     actor_network: TensorDictModule
diff --git a/torchrl/objectives/dqn.py b/torchrl/objectives/dqn.py
index 79e5b8de58c..47fc0508397 100644
--- a/torchrl/objectives/dqn.py
+++ b/torchrl/objectives/dqn.py
@@ -164,6 +164,7 @@ class _AcceptedKeys:
         done: NestedKey = "done"
         terminated: NestedKey = "terminated"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
     out_keys = ["loss"]
@@ -435,6 +436,7 @@ class _AcceptedKeys:
         terminated: NestedKey = "terminated"
         steps_to_next_obs: NestedKey = "steps_to_next_obs"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
 
diff --git a/torchrl/objectives/dreamer.py b/torchrl/objectives/dreamer.py
index b183e94ddd1..6c03461ce76 100644
--- a/torchrl/objectives/dreamer.py
+++ b/torchrl/objectives/dreamer.py
@@ -89,8 +89,13 @@ class _AcceptedKeys:
         pixels: NestedKey = "pixels"
         reco_pixels: NestedKey = "reco_pixels"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
 
+    decoder: TensorDictModule
+    reward_model: TensorDictModule
+    world_mdel: TensorDictModule
+
     def __init__(
         self,
         world_model: TensorDictModule,
@@ -238,9 +243,13 @@ class _AcceptedKeys:
         done: NestedKey = "done"
         terminated: NestedKey = "terminated"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TDLambda
 
+    value_model: TensorDictModule
+    actor_model: TensorDictModule
+
     def __init__(
         self,
         actor_model: TensorDictModule,
@@ -392,8 +401,11 @@ class _AcceptedKeys:
 
         value: NestedKey = "state_value"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
 
+    value_model: TensorDictModule
+
     def __init__(
         self,
         value_model: TensorDictModule,
diff --git a/torchrl/objectives/gail.py b/torchrl/objectives/gail.py
index dc26be70c54..bece855ad62 100644
--- a/torchrl/objectives/gail.py
+++ b/torchrl/objectives/gail.py
@@ -59,6 +59,7 @@ class _AcceptedKeys:
         collector_observation: NestedKey = "collector_observation"
         discriminator_pred: NestedKey = "d_logits"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
 
     discriminator_network: TensorDictModule
diff --git a/torchrl/objectives/iql.py b/torchrl/objectives/iql.py
index 774949407c1..ea84d59939a 100644
--- a/torchrl/objectives/iql.py
+++ b/torchrl/objectives/iql.py
@@ -233,6 +233,7 @@ class _AcceptedKeys:
         done: NestedKey = "done"
         terminated: NestedKey = "terminated"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
     out_keys = [
@@ -709,6 +710,7 @@ class _AcceptedKeys:
         done: NestedKey = "done"
         terminated: NestedKey = "terminated"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
     out_keys = [
diff --git a/torchrl/objectives/multiagent/qmixer.py b/torchrl/objectives/multiagent/qmixer.py
index fa9c0ad97fb..93b50f3e76b 100644
--- a/torchrl/objectives/multiagent/qmixer.py
+++ b/torchrl/objectives/multiagent/qmixer.py
@@ -179,6 +179,7 @@ class _AcceptedKeys:
         done: NestedKey = "done"
         terminated: NestedKey = "terminated"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
     out_keys = ["loss"]
diff --git a/torchrl/objectives/ppo.py b/torchrl/objectives/ppo.py
index b5b45393a06..9b41afd9afa 100644
--- a/torchrl/objectives/ppo.py
+++ b/torchrl/objectives/ppo.py
@@ -30,6 +30,7 @@
 from tensordict.utils import NestedKey
 from torch import distributions as d
 
+from torchrl._utils import _standardize
 from torchrl.objectives.common import LossModule
 
 from torchrl.objectives.utils import (
@@ -95,6 +96,9 @@ class PPOLoss(LossModule):
             Can be one of "l1", "l2" or "smooth_l1". Defaults to ``"smooth_l1"``.
         normalize_advantage (bool, optional): if ``True``, the advantage will be normalized
             before being used. Defaults to ``False``.
+        normalize_advantage_exclude_dims (Tuple[int], optional): dimensions to exclude from the advantage
+            standardization. Negative dimensions are valid. This is useful in multiagent (or multiobjective) settings
+            where the agent (or objective) dimension may be excluded from the reductions. Default: ().
         separate_losses (bool, optional): if ``True``, shared parameters between
             policy and critic will only be trained on the policy loss.
             Defaults to ``False``, i.e., gradients are propagated to shared
@@ -308,6 +312,7 @@ def __post_init__(self):
                     self.sample_log_prob = "action_log_prob"
 
     default_keys = _AcceptedKeys
+    tensor_keys: _AcceptedKeys
     default_value_estimator = ValueEstimators.GAE
 
     actor_network: ProbabilisticTensorDictModule
@@ -328,6 +333,7 @@ def __init__(
         critic_coef: float = 1.0,
         loss_critic_type: str = "smooth_l1",
         normalize_advantage: bool = False,
+        normalize_advantage_exclude_dims: Tuple[int] = (),
         gamma: float = None,
         separate_losses: bool = False,
         advantage_key: str = None,
@@ -398,6 +404,8 @@ def __init__(
             self.critic_coef = None
         self.loss_critic_type = loss_critic_type
         self.normalize_advantage = normalize_advantage
+        self.normalize_advantage_exclude_dims = normalize_advantage_exclude_dims
+
         if gamma is not None:
             raise TypeError(_GAMMA_LMBDA_DEPREC_ERROR)
         self._set_deprecated_ctor_keys(
@@ -656,9 +664,16 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
             )
             advantage = tensordict.get(self.tensor_keys.advantage)
         if self.normalize_advantage and advantage.numel() > 1:
-            loc = advantage.mean()
-            scale = advantage.std().clamp_min(1e-6)
-            advantage = (advantage - loc) / scale
+            if advantage.numel() > tensordict.batch_size.numel() and not len(
+                self.normalize_advantage_exclude_dims
+            ):
+                warnings.warn(
+                    "You requested advantage normalization and the advantage key has more dimensions"
+                    " than the tensordict batch. Make sure to pass `normalize_advantage_exclude_dims` "
+                    "if you want to keep any dimension independent while computing normalization statistics. "
+                    "If you are working in multi-agent/multi-objective settings this is highly suggested."
+                )
+            advantage = _standardize(advantage, self.normalize_advantage_exclude_dims)
 
         log_weight, dist, kl_approx = self._log_weight(tensordict)
         if is_tensor_collection(log_weight):
@@ -761,6 +776,9 @@ class ClipPPOLoss(PPOLoss):
             Can be one of "l1", "l2" or "smooth_l1". Defaults to ``"smooth_l1"``.
         normalize_advantage (bool, optional): if ``True``, the advantage will be normalized
             before being used. Defaults to ``False``.
+        normalize_advantage_exclude_dims (Tuple[int], optional): dimensions to exclude from the advantage
+            standardization. Negative dimensions are valid. This is useful in multiagent (or multiobjective) settings
+            where the agent (or objective) dimension may be excluded from the reductions. Default: ().
         separate_losses (bool, optional): if ``True``, shared parameters between
             policy and critic will only be trained on the policy loss.
             Defaults to ``False``, i.e., gradients are propagated to shared
@@ -852,6 +870,7 @@ def __init__(
         critic_coef: float = 1.0,
         loss_critic_type: str = "smooth_l1",
         normalize_advantage: bool = False,
+        normalize_advantage_exclude_dims: Tuple[int] = (),
         gamma: float = None,
         separate_losses: bool = False,
         reduction: str = None,
@@ -871,6 +890,7 @@ def __init__(
             critic_coef=critic_coef,
             loss_critic_type=loss_critic_type,
             normalize_advantage=normalize_advantage,
+            normalize_advantage_exclude_dims=normalize_advantage_exclude_dims,
             gamma=gamma,
             separate_losses=separate_losses,
             reduction=reduction,
@@ -921,9 +941,16 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
             )
             advantage = tensordict.get(self.tensor_keys.advantage)
         if self.normalize_advantage and advantage.numel() > 1:
-            loc = advantage.mean()
-            scale = advantage.std().clamp_min(1e-6)
-            advantage = (advantage - loc) / scale
+            if advantage.numel() > tensordict.batch_size.numel() and not len(
+                self.normalize_advantage_exclude_dims
+            ):
+                warnings.warn(
+                    "You requested advantage normalization and the advantage key has more dimensions"
+                    " than the tensordict batch. Make sure to pass `normalize_advantage_exclude_dims` "
+                    "if you want to keep any dimension independent while computing normalization statistics. "
+                    "If you are working in multi-agent/multi-objective settings this is highly suggested."
+                )
+            advantage = _standardize(advantage, self.normalize_advantage_exclude_dims)
 
         log_weight, dist, kl_approx = self._log_weight(tensordict)
         # ESS for logging
@@ -1007,6 +1034,9 @@ class KLPENPPOLoss(PPOLoss):
             Can be one of "l1", "l2" or "smooth_l1". Defaults to ``"smooth_l1"``.
         normalize_advantage (bool, optional): if ``True``, the advantage will be normalized
             before being used. Defaults to ``False``.
+        normalize_advantage_exclude_dims (Tuple[int], optional): dimensions to exclude from the advantage
+            standardization. Negative dimensions are valid. This is useful in multiagent (or multiobjective) settings
+            where the agent (or objective) dimension may be excluded from the reductions. Default: ().
         separate_losses (bool, optional): if ``True``, shared parameters between
             policy and critic will only be trained on the policy loss.
             Defaults to ``False``, i.e., gradients are propagated to shared
@@ -1100,6 +1130,7 @@ def __init__(
         critic_coef: float = 1.0,
         loss_critic_type: str = "smooth_l1",
         normalize_advantage: bool = False,
+        normalize_advantage_exclude_dims: Tuple[int] = (),
         gamma: float = None,
         separate_losses: bool = False,
         reduction: str = None,
@@ -1115,6 +1146,7 @@ def __init__(
             critic_coef=critic_coef,
             loss_critic_type=loss_critic_type,
             normalize_advantage=normalize_advantage,
+            normalize_advantage_exclude_dims=normalize_advantage_exclude_dims,
             gamma=gamma,
             separate_losses=separate_losses,
             reduction=reduction,
@@ -1203,9 +1235,17 @@ def forward(self, tensordict: TensorDictBase) -> TensorDict:
             )
             advantage = tensordict_copy.get(self.tensor_keys.advantage)
         if self.normalize_advantage and advantage.numel() > 1:
-            loc = advantage.mean()
-            scale = advantage.std().clamp_min(1e-6)
-            advantage = (advantage - loc) / scale
+            if advantage.numel() > tensordict.batch_size.numel() and not len(
+                self.normalize_advantage_exclude_dims
+            ):
+                warnings.warn(
+                    "You requested advantage normalization and the advantage key has more dimensions"
+                    " than the tensordict batch. Make sure to pass `normalize_advantage_exclude_dims` "
+                    "if you want to keep any dimension independent while computing normalization statistics. "
+                    "If you are working in multi-agent/multi-objective settings this is highly suggested."
+                )
+            advantage = _standardize(advantage, self.normalize_advantage_exclude_dims)
+
         log_weight, dist, kl_approx = self._log_weight(tensordict_copy)
         neg_loss = log_weight.exp() * advantage
         if is_tensor_collection(neg_loss):
diff --git a/torchrl/objectives/reinforce.py b/torchrl/objectives/reinforce.py
index 3a9a9bac791..0cac502b347 100644
--- a/torchrl/objectives/reinforce.py
+++ b/torchrl/objectives/reinforce.py
@@ -224,6 +224,7 @@ def __post_init__(self):
                 else:
                     self.sample_log_prob = "action_log_prob"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.GAE
     out_keys = ["loss_actor", "loss_value"]
diff --git a/torchrl/objectives/sac.py b/torchrl/objectives/sac.py
index 9b5d9b35d64..9d60d51334a 100644
--- a/torchrl/objectives/sac.py
+++ b/torchrl/objectives/sac.py
@@ -301,6 +301,7 @@ def __post_init__(self):
                     self.log_prob = "action_log_prob"
 
     default_keys = _AcceptedKeys
+    tensor_keys: _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
 
     actor_network: TensorDictModule
@@ -1046,6 +1047,7 @@ class _AcceptedKeys:
         terminated: NestedKey = "terminated"
         log_prob: NestedKey = "log_prob"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
     delay_actor: bool = False
diff --git a/torchrl/objectives/td3.py b/torchrl/objectives/td3.py
index 0fcfa5e8351..f5d67eea164 100644
--- a/torchrl/objectives/td3.py
+++ b/torchrl/objectives/td3.py
@@ -204,6 +204,7 @@ class _AcceptedKeys:
         done: NestedKey = "done"
         terminated: NestedKey = "terminated"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
     out_keys = [
diff --git a/torchrl/objectives/td3_bc.py b/torchrl/objectives/td3_bc.py
index a217240d96f..deb5b844500 100644
--- a/torchrl/objectives/td3_bc.py
+++ b/torchrl/objectives/td3_bc.py
@@ -217,6 +217,7 @@ class _AcceptedKeys:
         done: NestedKey = "done"
         terminated: NestedKey = "terminated"
 
+    tensor_keys: _AcceptedKeys
     default_keys = _AcceptedKeys
     default_value_estimator = ValueEstimators.TD0
     out_keys = [
diff --git a/torchrl/objectives/value/advantages.py b/torchrl/objectives/value/advantages.py
index dbb269e2e8b..dd253a6d908 100644
--- a/torchrl/objectives/value/advantages.py
+++ b/torchrl/objectives/value/advantages.py
@@ -152,6 +152,7 @@ def __post_init__(self):
                     self.sample_log_prob = "action_log_prob"
 
     default_keys = _AcceptedKeys
+    tensor_keys: _AcceptedKeys
     value_network: Union[TensorDictModule, Callable]
     _vmap_randomness = None