fix links

Author: diberry

articles/cognitive-services/personalizer/concept-active-inactive-events.md

@@ -15,7 +15,7 @@ It is important the that the learning loop know the actual type of event. An ina
 
 ## Typical active events scenario
 
-When your application calls the Rank API, you receive the action which the application should show in the **rewardActionId** field.  From that moment, Personalizer expects a Reward call with a reward score that has the same eventId. The reward score is used to train the model for future Rank calls. If no Reward call is received for the eventId, a default reward is applied. [Default rewards](how-to-settings#configure-rewards-for-the-feedback-loop-based-on-use-case) are set on your Personalizer resource in the Azure portal.
+When your application calls the Rank API, you receive the action which the application should show in the **rewardActionId** field.  From that moment, Personalizer expects a Reward call with a reward score that has the same eventId. The reward score is used to train the model for future Rank calls. If no Reward call is received for the eventId, a default reward is applied. [Default rewards](how-to-settings.md#configure-rewards-for-the-feedback-loop-based-on-use-case) are set on your Personalizer resource in the Azure portal.
 
 ## Other event type scenarios
 

articles/cognitive-services/personalizer/concept-active-learning.md

@@ -9,7 +9,7 @@ ms.date: 02/20/2020
 
 Learning settings determine the *hyperparameters* of the model training. Two models of the same data that are trained on different learning settings will end up different.
 
-[Learning policy and settings](how-to-settings#configure-rewards-for-the-feedback-loop-based-on-use-case) are set on your Personalizer resource in the Azure portal.
+[Learning policy and settings](how-to-settings.md#configure-rewards-for-the-feedback-loop-based-on-use-case) are set on your Personalizer resource in the Azure portal.
 
 ### Import and export learning policies
 

articles/cognitive-services/personalizer/concept-rewards.md

@@ -11,7 +11,7 @@ The reward score indicates how well the personalization choice, [RewardActionID]
 
 Personalizer trains its machine learning models by evaluating the rewards.
 
-Learn [how to](how-to-settings.md#configure-rewards-for-the-feedback-loop-based-on-use-case) configure the default reward score in the Azure portal for your Personalizer resource.
+Learn [how to](how-to-settings.md#configure-rewards-for-the-feedback-loop) configure the default reward score in the Azure portal for your Personalizer resource.
 
 ## Use Reward API to send reward score to Personalizer
 

articles/cognitive-services/personalizer/how-personalizer-works.md

@@ -14,7 +14,7 @@ The Personalizer reource, your _learning loop_, uses machine learning to build t
 You send _actions with features_ and _context features_ to the Rank API. The **Rank** API decides to use either:
 
 * _Exploit_: The current model to decide the best action based on past data.
-* _Explore_: Select a different action instead of the top action. You [configure this percentage](how-to-settings#configure-exploration-to-allow-the-learning-loop-to-adapt) for your Personalizer resource in the Azure portal.
+* _Explore_: Select a different action instead of the top action. You [configure this percentage](how-to-settings.md#configure-exploration-to-allow-the-learning-loop-to-adapt) for your Personalizer resource in the Azure portal.
 
 You determine the reward score and send that score to the Reward API. The **Reward** API: