diff --git a/tianshou/highlevel/experiment.py b/tianshou/highlevel/experiment.py
index 4db4655..fa1cc0e 100644
--- a/tianshou/highlevel/experiment.py
+++ b/tianshou/highlevel/experiment.py
@@ -187,7 +187,7 @@ class Experiment(ToStringMixin):
     ) -> ExperimentResult:
         """:param experiment_name: the experiment name, which corresponds to the directory (within the logging
             directory) where all results associated with the experiment will be saved.
-            The name may contain path separators (os.path.sep, used by os.path.join), in which case
+            The name may contain path separators (i.e. `os.path.sep`, as used by `os.path.join`), in which case
             a nested directory structure will be created.
             If None, use a name containing the current date and time.
         :param logger_run_id: Run identifier to use for logger initialization/resumption (applies when
diff --git a/tianshou/highlevel/logger.py b/tianshou/highlevel/logger.py
index 3458b6e..396613a 100644
--- a/tianshou/highlevel/logger.py
+++ b/tianshou/highlevel/logger.py
@@ -20,7 +20,7 @@ class LoggerFactory(ToStringMixin, ABC):
         config_dict: dict,
     ) -> TLogger:
         """:param log_dir: path to the directory in which log data is to be stored
-        :param experiment_name: the name of the job, which may contain os.path.sep
+        :param experiment_name: the name of the job, which may contain `os.path.sep`
         :param run_id: a unique name, which, depending on the logging framework, may be used to identify the logger
         :param config_dict: a dictionary with data that is to be logged
         :return: the logger
diff --git a/tianshou/highlevel/params/lr_scheduler.py b/tianshou/highlevel/params/lr_scheduler.py
index 93b1ce0..286df4c 100644
--- a/tianshou/highlevel/params/lr_scheduler.py
+++ b/tianshou/highlevel/params/lr_scheduler.py
@@ -9,7 +9,7 @@ from tianshou.utils.string import ToStringMixin
 
 
 class LRSchedulerFactory(ToStringMixin, ABC):
-    """Factory for the createion of a learning rate scheduler."""
+    """Factory for the creation of a learning rate scheduler."""
 
     @abstractmethod
     def create_scheduler(self, optim: torch.optim.Optimizer) -> LRScheduler: