# 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/1.9.1/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.

As an example, let’s say you have an `out_of_scope` intent which describes off-topic user messages.

```yaml
* 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.

To alter this behavior with the `--augmentation` flag, use the `augmentation_factor`. The `augmentation_factor` determines how many augmented stories are subsampled during training.

```shell
--augmentation 0
```

Disables all augmentation behavior.

## Action Selection

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

In the case that two policies predict with equal confidence, 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.

## Keras Policy

The `KerasPolicy` uses a neural network implemented in [Keras](http://keras.io/) to select the next action.

```python
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."""
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Masking, LSTM, Dense, TimeDistributed, Activation

# 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]))
```

### Embedding Policy
> Warning: `EmbeddingPolicy` was renamed to `TEDPolicy`. Please use [TED Policy](https://legacy-docs-v1.rasa.com/1.9.1/core/policies/#ted-policy) instead.

## Mapping Policy

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

```yaml
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:**
```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.