diff --git a/tianshou/utils/net/continuous.py b/tianshou/utils/net/continuous.py
index 6cd4a0f..0b28f98 100644
--- a/tianshou/utils/net/continuous.py
+++ b/tianshou/utils/net/continuous.py
@@ -15,6 +15,7 @@ from tianshou.utils.net.common import (
     TLinearLayer,
     get_output_dim,
 )
+from tianshou.utils.pickle import setstate
 
 SIGMA_MIN = -20
 SIGMA_MAX = 2
@@ -109,6 +110,9 @@ class Critic(CriticBase):
         `preprocess_net`. Only used when `preprocess_net` does not have the attribute `output_dim`.
     :param linear_layer: use this module as linear layer.
     :param flatten_input: whether to flatten input data for the last layer.
+    :param apply_preprocess_net_to_obs_only: whether to apply `preprocess_net` to the observations only (before
+        concatenating with the action) - and without the observations being modified in any way beforehand.
+        This allows the actor's preprocessing network to be reused for the critic.
 
     For advanced usage (how to customize the network), please refer to
     :ref:`build_the_network`.
@@ -122,11 +126,13 @@ class Critic(CriticBase):
         preprocess_net_output_dim: int | None = None,
         linear_layer: TLinearLayer = nn.Linear,
         flatten_input: bool = True,
+        apply_preprocess_net_to_obs_only: bool = False,
     ) -> None:
         super().__init__()
         self.device = device
         self.preprocess = preprocess_net
         self.output_dim = 1
+        self.apply_preprocess_net_to_obs_only = apply_preprocess_net_to_obs_only
         input_dim = get_output_dim(preprocess_net, preprocess_net_output_dim)
         self.last = MLP(
             input_dim,
@@ -137,6 +143,14 @@ class Critic(CriticBase):
             flatten_input=flatten_input,
         )
 
+    def __setstate__(self, state: dict) -> None:
+        setstate(
+            Critic,
+            self,
+            state,
+            new_default_properties={"apply_preprocess_net_to_obs_only": False},
+        )
+
     def forward(
         self,
         obs: np.ndarray | torch.Tensor,
@@ -148,7 +162,10 @@ class Critic(CriticBase):
             obs,
             device=self.device,
             dtype=torch.float32,
-        ).flatten(1)
+        )
+        if self.apply_preprocess_net_to_obs_only:
+            obs, _ = self.preprocess(obs)
+        obs = obs.flatten(1)
         if act is not None:
             act = torch.as_tensor(
                 act,
@@ -156,8 +173,9 @@ class Critic(CriticBase):
                 dtype=torch.float32,
             ).flatten(1)
             obs = torch.cat([obs, act], dim=1)
-        values_B, hidden_BH = self.preprocess(obs)
-        return self.last(values_B)
+        if not self.apply_preprocess_net_to_obs_only:
+            obs, _ = self.preprocess(obs)
+        return self.last(obs)
 
 
 class ActorProb(BaseActor):