Policies

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. In the example below, the last two lines show how to use a custom policy class and pass arguments to it.

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.

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

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.

Action Selection

At every turn, each policy defined in your configuration will predict a next action with a certain confidence level.

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:
    # Implementation details...
    pass

Mapping Policy

The MappingPolicy can be used to directly map intents to actions.

Memoization Policy

The MemoizationPolicy just memorizes the conversations in your training data.

Augmented Memoization Policy

The AugmentedMemoizationPolicy remembers examples from training stories for up to max_history turns.

Fallback Policy

The FallbackPolicy invokes a fallback action if certain conditions are met.

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.

Form Policy

The FormPolicy is an extension of the MemoizationPolicy which handles the filling of forms.