diff --git a/tianshou/policy/modelfree/sac.py b/tianshou/policy/modelfree/sac.py
index ac5b3e0..abe707d 100644
--- a/tianshou/policy/modelfree/sac.py
+++ b/tianshou/policy/modelfree/sac.py
@@ -5,7 +5,7 @@ import numpy as np
 import torch
 from torch.distributions import Independent, Normal
 
-from tianshou.data import Batch, ReplayBuffer, to_torch_as
+from tianshou.data import Batch, ReplayBuffer
 from tianshou.exploration import BaseNoise
 from tianshou.policy import DDPGPolicy
 
@@ -121,15 +121,9 @@ class SACPolicy(DDPGPolicy):
         # apply correction for Tanh squashing when computing logprob from Gaussian
         # You can check out the original SAC paper (arXiv 1801.01290): Eq 21.
         # in appendix C to get some understanding of this equation.
-        if self.action_scaling and self.action_space is not None:
-            low, high = self.action_space.low, self.action_space.high  # type: ignore
-            action_scale = to_torch_as((high - low) / 2.0, act)
-        else:
-            action_scale = 1.0  # type: ignore
         squashed_action = torch.tanh(act)
-        log_prob = log_prob - torch.log(
-            action_scale * (1 - squashed_action.pow(2)) + self.__eps
-        ).sum(-1, keepdim=True)
+        log_prob = log_prob - torch.log((1 - squashed_action.pow(2)) +
+                                        self.__eps).sum(-1, keepdim=True)
         return Batch(
             logits=logits,
             act=squashed_action,