# 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/1.8.1/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. In the example below, the last two lines show how to use a custom policy class and pass arguments to it.

```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/1.8.1/api/core-featurization/#featurization-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:

```yaml
# 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:

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

### 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 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.:

```yaml
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`.