# 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/1.10.24/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`.

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/1.10.24/api/core-featurization/#featurization-conversations) for details.

As an example, let’s say you have an `out_of_scope` intent which describes off-topic user messages. If your bot sees this intent multiple times in a row, you might want to tell the user what you can help them with. So your story might look like this:

```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.

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 behavior 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.

### 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.

## 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:
    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
```

## Embedding Policy

> Warning
>
> `EmbeddingPolicy` was renamed to `TEDPolicy`. Please use [TED Policy](https://legacy-docs-v1.rasa.com/1.10.24/core/policies/#ted-policy) instead of `EmbeddingPolicy` in your policy configuration.