[Minor] Fix typos in advantages.py (#2492)

Co-authored-by: Louis Faury <louis.faury@helsing.ai>

torchrl/objectives/value/advantages.py

@@ -1177,17 +1177,17 @@ class GAE(ValueEstimatorBase):
         device (torch.device, optional): device of the module.
         time_dim (int, optional): the dimension corresponding to the time
             in the input tensordict. If not provided, defaults to the dimension
-            markes with the ``"time"`` name if any, and to the last dimension
+            marked with the ``"time"`` name if any, and to the last dimension
             otherwise. Can be overridden during a call to
             :meth:`~.value_estimate`.
             Negative dimensions are considered with respect to the input
             tensordict.
 
-    GAE will return an :obj:`"advantage"` entry containing the advange value. It will also
+    GAE will return an :obj:`"advantage"` entry containing the advantage value. It will also
     return a :obj:`"value_target"` entry with the return value that is to be used
     to train the value network. Finally, if :obj:`gradient_mode` is ``True``,
     an additional and differentiable :obj:`"value_error"` entry will be returned,
-    which simple represents the difference between the return and the value network
+    which simply represents the difference between the return and the value network
     output (i.e. an additional distance loss should be applied to that signed value).
 
     .. note::
@@ -1262,7 +1262,7 @@ def forward(
                 target params to be passed to the functional value network module.
             time_dim (int, optional): the dimension corresponding to the time
                 in the input tensordict. If not provided, defaults to the dimension
-                markes with the ``"time"`` name if any, and to the last dimension
+                marked with the ``"time"`` name if any, and to the last dimension
                 otherwise.
                 Negative dimensions are considered with respect to the input
                 tensordict.
@@ -1310,7 +1310,7 @@ def forward(
         """
         if tensordict.batch_dims < 1:
             raise RuntimeError(
-                "Expected input tensordict to have at least one dimensions, got "
+                "Expected input tensordict to have at least one dimension, got "
                 f"tensordict.batch_size = {tensordict.batch_size}"
             )
         reward = tensordict.get(("next", self.tensor_keys.reward))