diff --git a/pearl/neural_networks/sequential_decision_making/actor_networks.py b/pearl/neural_networks/sequential_decision_making/actor_networks.py
index 21c05419..be614a5b 100644
--- a/pearl/neural_networks/sequential_decision_making/actor_networks.py
+++ b/pearl/neural_networks/sequential_decision_making/actor_networks.py
@@ -50,6 +50,20 @@ def action_scaling(
     return centered_and_scaled_action
 
 
+def action_unscaling(
+    action_space: ActionSpace, input_action: torch.Tensor
+) -> torch.Tensor:
+    """
+    The reverse operation of action_scaling
+    """
+    assert isinstance(action_space, BoxActionSpace)
+    device = input_action.device
+    low = action_space.low.clone().detach().to(device)
+    high = action_space.high.clone().detach().to(device)
+    unscaled_action = (((input_action - low) / (high - low)) * 2) - 1
+    return unscaled_action
+
+
 def noise_scaling(action_space: ActionSpace, input_noise: torch.Tensor) -> torch.Tensor:
     """
     This function rescales any input vector from [-1, 1]^{action_dim} to [low, high]^{action_dim}.
@@ -473,16 +487,17 @@ def get_log_probability(
         std = log_std.exp()
         normal = Normal(mean, std)
 
-        # assume that input actions are in [-1, 1]^d
-        # TODO: change this to add a transform for unscaling and uncentering depending on the
-        # action space
-        unscaled_action_batch = torch.clip(action_batch, -1 + epsilon, 1 - epsilon)
+        normalized_action_batch = torch.clip(
+            action_unscaling(self._action_space, action_batch),
+            -1 + epsilon,
+            1 - epsilon,
+        )
 
         # transform actions from [-1, 1]^d to [-inf, inf]^d
-        unnormalized_action_batch = torch.atanh(unscaled_action_batch)
+        unnormalized_action_batch = torch.atanh(normalized_action_batch)
         log_prob = normal.log_prob(unnormalized_action_batch)
         log_prob -= torch.log(
-            self._action_bound * (1 - unscaled_action_batch.pow(2)) + epsilon
+            self._action_bound * (1 - normalized_action_batch.pow(2)) + epsilon
         )
 
         # for multi-dimensional action space, sum log probabilities over individual