Documentation: Escape the % escape (#783)

src/orion/algo/pbt/exploit.py

@@ -171,7 +171,7 @@ class TruncateExploit(BaseExploit):
 
     If the given trial is under a ``truncation_quantile`` compared to all other trials that
     has reached the same fidelity level, then a new candidate trial is selected for forking.
-    The new candidate is selected from a pool of best ``candidate_pool_ratio``\% of the available
+    The new candidate is selected from a pool of best ``candidate_pool_ratio``\\% of the available
     trials at the same fidelity level.
 
     If there are less than ``min_forking_population`` trials that have reached the fidelity level
@@ -189,7 +189,7 @@ class TruncateExploit(BaseExploit):
         candidate is considered for forking. Default: 0.8
     candidate_pool_ratio: float, optional
         When choosing another candidate for forking, it will be randomly selected from the
-        best ``candidate_pool_ratio``\% of the available trials. Default: 0.2
+        best ``candidate_pool_ratio``\\% of the available trials. Default: 0.2
 
     """
 
@@ -208,7 +208,7 @@ def __call__(self, rng, trial, lineages):
 
         If the given trial is under a ``self.truncation_quantile`` compared to all other trials that
         has reached the same fidelity level, then a new candidate trial is selected for forking.
-        The new candidate is selected from a pool of best ``self.candidate_pool_ratio``\% of the
+        The new candidate is selected from a pool of best ``self.candidate_pool_ratio``\\% of the
         available trials at the same fidelity level.
 
         If there are less than ``self.min_forking_population`` trials that have reached the fidelity
@@ -333,7 +333,7 @@ def __call__(self, rng, trial, lineages):
         If the given trial is under a ``self.truncation_quantile`` compared to all other best
         trials with lower or equal fidelity level,
         then a new candidate trial is selected for forking.
-        The new candidate is selected from a pool of best ``self.candidate_pool_ratio``\% of the
+        The new candidate is selected from a pool of best ``self.candidate_pool_ratio``\\% of the
         best trials with lower or equal fidelity level. See class description for more
         explanation on the rationale.