Update docstring of normalize reward

gymnasium/wrappers/stateful_reward.py

@@ -19,7 +19,7 @@
 class NormalizeReward(
     gym.Wrapper[ObsType, ActType, ObsType, ActType], gym.utils.RecordConstructorArgs
 ):
-    r"""Normalizes immediate rewards such that their exponential moving average has a fixed variance.
+    r"""Scales immediate rewards such that their exponential moving average has a fixed variance.
 
     The exponential moving average will have variance :math:`(1 - \gamma)^2`.
 
@@ -29,6 +29,10 @@ class NormalizeReward(
 
     A vector version of the wrapper exists :class:`gymnasium.wrappers.vector.NormalizeReward`.
 
+    Important note:
+        Contrary to what the name suggests, this wrapper does not normalize the rewards to have a mean of 0 and a standard
+        deviation of 1. Instead, it scales the rewards such that their exponential moving average has a fixed variance.
+
     Note:
         In v0.27, NormalizeReward was updated as the forward discounted reward estimate was incorrectly computed in Gym v0.25+.
         For more detail, read [#3154](https://github.com/openai/gym/pull/3152).

gymnasium/wrappers/vector/stateful_reward.py

@@ -18,14 +18,18 @@
 
 
 class NormalizeReward(VectorWrapper, gym.utils.RecordConstructorArgs):
-    r"""This wrapper will normalize immediate rewards s.t. their exponential moving average has a fixed variance.
+    r"""This wrapper will scale immediate rewards s.t. their exponential moving average has a fixed variance.
 
     The exponential moving average will have variance :math:`(1 - \gamma)^2`.
 
     The property `_update_running_mean` allows to freeze/continue the running mean calculation of the reward
     statistics. If `True` (default), the `RunningMeanStd` will get updated every time `self.normalize()` is called.
     If False, the calculated statistics are used but not updated anymore; this may be used during evaluation.
 
+    Important note:
+        Contrary to what the name suggests, this wrapper does not normalize the rewards to have a mean of 0 and a standard
+        deviation of 1. Instead, it scales the rewards such that their exponential moving average has a fixed variance.
+
     Note:
         The scaling depends on past trajectories and rewards will not be scaled correctly if the wrapper was newly
         instantiated or the policy was changed recently.