Policies

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. 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.

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. This is because if you have stories like:

# thanks
* thankyou
   - utter_youarewelcome

# bye
* goodbye
   - utter_goodbye

You actually want to teach your policy to ignore the dialogue history when it isn’t relevant and just respond with the same action no matter what happened before.

You can alter this behaviour with the --augmentation flag. Which allows you to set the augmentation_factor. The augmentation_factor determines how many augmented stories are subsampled during training. The augmented stories are subsampled before training since their number can quickly become very large, and we want to limit it. The number of sampled stories is augmentation_factor x10. By default augmentation is set to 20, resulting in a maximum of 200 augmented stories.

--augmentation 0 disables all augmentation behavior. 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.

In the case that two policies predict with equal confidence (for example, the Memoization and Mapping Policies always predict with confidence of either 0 or 1), the priority of the policies is considered. Rasa policies have default priorities that are set to ensure the expected outcome in the case of a tie. They look like this, where higher numbers have higher priority:

  1. FormPolicy
  2. FallbackPolicy and TwoStageFallbackPolicy
  3. MemoizationPolicy and AugmentedMemoizationPolicy
  4. MappingPolicy
  5. TEDPolicy, EmbeddingPolicy, KerasPolicy, and SklearnPolicy

Keras Policy

The KerasPolicy uses a neural network implemented in Keras to select the next action. The default architecture is based on an LSTM, but you can override the KerasPolicy.model_architecture method to implement your own architecture.

def model_architecture(self, input_shape: Tuple[int, int], output_shape: Tuple[int, Optional[int]]) -> tf.keras.models.Sequential:
    # Build Model
    model = Sequential()
    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]))
    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. 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.