These docs are for version 1.x of Rasa Open Source.

Policies

• Configuring Policies
  • Max History
  • Data Augmentation
• Action Selection
• Keras Policy
• Embedding Policy
• TED Policy
• Mapping Policy
• Memoization Policy
• Augmented Memoization Policy
• Fallback Policy
• Two-Stage Fallback Policy
• Form Policy

Configuring Policies

The rasa.core.policies.Policy class decides which action to take at every step in the conversation.

There are different policies to choose from, and you can include multiple policies in a single rasa.core.agent.Agent.

Note

Per default a maximum of 10 next actions can be predicted by the agent after every user message. To update this value you can set the environment variable MAX_NUMBER_OF_PREDICTIONS to the desired number of maximum predictions.

Your project’s config.yml file takes a policies key which you can use to customize the policies your assistant uses.

policies:
  - name: "KerasPolicy"
    featurizer:
    - name: MaxHistoryTrackerFeaturizer
      max_history: 5
      state_featurizer:
        - name: BinarySingleStateFeaturizer
  - name: "MemoizationPolicy"
    max_history: 5
  - name: "FallbackPolicy"
    nlu_threshold: 0.4
    core_threshold: 0.3
    fallback_action_name: "my_fallback_action"
  - name: "path.to.your.policy.class"
    arg1: "..."

Max History

One important hyperparameter for Rasa Core policies is the max_history. This controls how much dialogue history the model looks at to decide which action to take next.

You can set the max_history by passing it to your policy’s Featurizer in the policy configuration yaml file.

Note

Only the MaxHistoryTrackerFeaturizer uses a max history, whereas the FullDialogueTrackerFeaturizer always looks at the full conversation history. See Featurization of Conversations for details.

As an example, let’s say you have an out_of_scope intent which describes off-topic user messages. If your bot sees this intent multiple times in a row, you might want to tell the user what you can help them with. So your story might look like this:

* out_of_scope
   - utter_default
* out_of_scope
   - utter_default
* out_of_scope
   - utter_help_message

For Rasa Core to learn this pattern, the max_history has to be at least 4.

Data Augmentation

When you train a model, by default Rasa Core will create longer stories by randomly gluing together the ones in your stories files.

Note

The memoization based policies are not affected by augmentation (independent of the augmentation_factor) and will automatically ignore all augmented stories.

Action Selection

At every turn, each policy defined in your configuration will predict a next action with a certain confidence level. For more information about how each policy makes its decision, read into the policy’s description below. The bot’s next action is then decided by the policy that predicts with the highest confidence.

Keras Policy

The KerasPolicy uses a neural network implemented in Keras to select the next action.

def model_architecture(
    self, input_shape: Tuple[int, int], output_shape: Tuple[int, Optional[int]]
) -> tf.keras.models.Sequential:
    """Build a keras model and return a compiled model."""
    ...

Mapping Policy

The MappingPolicy can be used to directly map intents to actions. The mappings are assigned by giving an intent the property triggers, e.g.:

intents:
 - ask_is_bot:
     triggers: action_is_bot

Memoization Policy

The MemoizationPolicy just memorizes the conversations in your training data. It predicts the next action with confidence 1.0 if this exact conversation exists in the training data, otherwise it predicts None with confidence 0.0.

Fallback Policy

The FallbackPolicy invokes a fallback action if at least one of the following occurs:

1. The intent recognition has a confidence below nlu_threshold.
2. The highest ranked intent differs in confidence with the second highest ranked intent by less than ambiguity_threshold.
3. None of the dialogue policies predict an action with confidence higher than core_threshold.

Configuration:

policies:
   - name: "FallbackPolicy"
     nlu_threshold: 0.3
     ambiguity_threshold: 0.1
     core_threshold: 0.3
     fallback_action_name: 'action_default_fallback'

Two-Stage Fallback Policy

The TwoStageFallbackPolicy handles low NLU confidence in multiple stages by trying to disambiguate the user input.

Configuration:

policies:
   - name: TwoStageFallbackPolicy
     nlu_threshold: 0.3
     ambiguity_threshold: 0.1
     core_threshold: 0.3
     fallback_core_action_name: "action_default_fallback"
     fallback_nlu_action_name: "action_default_fallback"
     deny_suggestion_intent_name: "out_of_scope"

Form Policy

The FormPolicy is an extension of the MemoizationPolicy which handles the filling of forms. Once a FormAction is called, the FormPolicy will continually predict the FormAction until all required slots in the form are filled.