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

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.

Note

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

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.

You can alter this behavior with the --augmentation flag.

Action Selection

At every turn, each policy defined in your configuration will predict a next action with a certain confidence level. The bot’s next action is then decided by the policy that predicts with the highest confidence.

If you create your own policy, use these priorities as a guide for figuring out the priority of your policy.

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:
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import (
        Masking,
        LSTM,
        Dense,
        TimeDistributed,
        Activation,
    )

model = Sequential()

if len(output_shape) == 1:
        model.add(Masking(mask_value=-1, input_shape=input_shape))
        model.add(LSTM(self.rnn_size, dropout=0.2))
        model.add(Dense(input_dim=self.rnn_size, units=output_shape[-1]))
    elif len(output_shape) == 2:
        model.add(Masking(mask_value=-1, input_shape=(None, input_shape[1])))
        model.add(LSTM(self.rnn_size, return_sequences=True, dropout=0.2))
        model.add(TimeDistributed(Dense(units=output_shape[-1])))
    else:
        raise ValueError(
            "Cannot construct the model because"
            "length of output_shape = {} "
            "should be 1 or 2."
            "".format(len(output_shape))
        )

model.add(Activation("softmax"))
    model.compile(
        loss="categorical_crossentropy", optimizer="rmsprop", metrics=["accuracy"]
    )
    return model

Mapping Policy

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

intents:
 - ask_is_bot:
     triggers: action_is_bot

Memoization Policy

The MemoizationPolicy just memorizes the conversations in your training data.

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.