# 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`](https://legacy-docs-v1.rasa.com/1.10.26/api/agent/#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.

```yaml
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](https://legacy-docs-v1.rasa.com/1.10.26/api/core-featurization/#featurization-conversations) for details.

#### Example

```yaml
# Rasa conversation pattern
* out_of_scope
   - utter_default
* out_of_scope
   - utter_default
* out_of_scope
   - utter_help_message
```

### 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.

```yaml
# thanks
* thankyou
   - utter_youarewelcome

# bye
* goodbye
   - utter_goodbye
```

### 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](http://keras.io/) 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.

```python
def model_architecture(self, input_shape: Tuple[int, int], output_shape: Tuple[int, Optional[int]]) -> tf.keras.models.Sequential:
    # Build Keras Model
    model = Sequential()
    # Add layers
    # Return compiled model
    return model
```

### Action Selection Details
The bot’s next action is then decided by the policy that predicts with the highest confidence.

### Fallback Policy
The `FallbackPolicy` invokes a [fallback action](https://legacy-docs-v1.rasa.com/1.10.26/core/fallback-actions/#fallback-actions) if a specific condition 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 Example
```yaml
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.

### 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.

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