# 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/1.7.2/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/1.7.2/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.

If you increase your `max_history`, your model will become bigger and training will take longer. If you have some information that should affect the dialogue very far into the future, you should store it as a slot. Slot information is always available for every featurizer.

### 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. `EmbeddingPolicy`, `KerasPolicy`, and `SklearnPolicy`

This priority hierarchy ensures that, for example, if there is an intent with a mapped action, but the NLU confidence is not above the `nlu_threshold`, the bot will still fall back. In general, it is not recommended to have more than one policy per priority level, and some policies on the same priority level, such as the two fallback policies, strictly cannot be used in tandem.

If you create your own policy, use these priorities as a guide for figuring out the priority of your policy. If your policy is a machine learning policy, it should most likely have priority 1, the same as the Rasa machine learning policies.

### 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 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()
    
    # the shape of the y vector of the labels,
    # determines which output from rnn will be used
    # to calculate the loss
    if len(output_shape) == 1:
        # y is (num examples, num features) so
        # only the last output from the rnn is used to
        # calculate the loss
        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:
        # y is (num examples, max_dialogue_len, num features) so
        # all the outputs from the rnn are used to
        # calculate the loss, therefore a sequence is returned and
        # time distributed layer is used

# the first value in input_shape is max dialogue_len,
        # it is set to None, to allow dynamic_rnn creation
        # during prediction
        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"]
    )

if obtain_verbosity() > 0:
        model.summary()

return model
```

and the training is run here:

```python
def train(
    self,
    training_trackers: List[DialogueStateTracker],
    domain: Domain,
    **kwargs: Any,
) -> None:

# set numpy random seed
    np.random.seed(self.random_seed)

training_data = self.featurize_for_training(training_trackers, domain, **kwargs)
    # noinspection PyPep8Naming
    shuffled_X, shuffled_y = training_data.shuffled_X_y()

self.graph = tf.Graph()
    with self.graph.as_default():
        # set random seed in tf
        tf.set_random_seed(self.random_seed)
        self.session = tf.compat.v1.Session(config=self._tf_config)

with self.session.as_default():
            if self.model is None:
                self.model = self.model_architecture(
                    shuffled_X.shape[1:], shuffled_y.shape[1:]
                )

logger.info(
                "Fitting model with {} total samples and a "
                "validation split of {}"
                "".format(training_data.num_examples(), self.validation_split)
            )

# filter out kwargs that cannot be passed to fit
            self._train_params = self._get_valid_params(
                self.model.fit, **self._train_params
            )

self.model.fit(
                shuffled_X,
                shuffled_y,
                epochs=self.epochs,
                batch_size=self.batch_size,
                shuffle=False,
                verbose=obtain_verbosity(),
                **self._train_params,
            )
            # the default parameter for epochs in keras fit is 1
            self.current_epoch = self.defaults.get("epochs", 1)
            logger.info("Done fitting keras policy model")
```

You can implement the model of your choice by overriding these methods, or initialize `KerasPolicy` with pre-defined `keras model`.

In order to get reproducible training results for the same inputs you can set the `random_seed` attribute of the `KerasPolicy` to any integer.

### Embedding Policy

Transformer Embedding Dialogue Policy (TEDP)

Transformer version of the Recurrent Embedding Dialogue Policy (REDP) used in our paper: [https://arxiv.org/abs/1811.11707](https://arxiv.org/abs/1811.11707)

This policy has a pre-defined architecture, which comprises the following steps:

> - concatenate user input (user intent and entities),
> previous system action, slots and active form for each time step into an input vector to pre-transformer embedding layer;
> - feed it to transformer;
> - apply a dense layer to the output of the transformer to get embeddings of a dialogue for each time step;
> - apply a dense layer to create embeddings for system actions for each time step;
> - calculate the similarity between the dialogue embedding and embedded system actions.
> This step is based on the [StarSpace](https://arxiv.org/abs/1709.03856) idea.

It is recommended to use `state_featurizer=LabelTokenizerSingleStateFeaturizer(...)` (see [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.7.2/api/core-featurization/#featurization-conversations) for details).

**Configuration:**
> Configuration parameters can be passed as parameters to the `EmbeddingPolicy` within the policy configuration file.
> Warning
> Pass an appropriate number of `epochs` to the `EmbeddingPolicy`, otherwise the policy will be trained only for `1` epoch.
> The algorithm also has hyper-parameters to control:
> - neural network’s architecture:
> - `hidden_layers_sizes_b` sets a list of hidden layers sizes before embedding layer for system actions, the number of hidden layers is equal to the length of the list;
> - `transformer_size` sets the number of units in the transfomer;
> - `num_transformer_layers` sets the number of transformer layers;
> - `pos_encoding` sets the type of positional encoding in transformer, it should be either `timing` or `emb`;
> - `max_seq_length` sets maximum sequence length if embedding positional encodings are used;
> - `num_heads` sets the number of heads in multihead attention;
> - training:
> - `batch_size` sets the number of training examples in one forward/backward pass, the higher the batch size, the more memory space you’ll need;
> - `batch_strategy` sets the type of batching strategy, it should be either `sequence` or `balanced`;
> - `epochs` sets the number of times the algorithm will see training data, where one `epoch` equals one forward pass and one backward pass of all the training examples;
> - `random_seed` if set to any int will get reproducible training results for the same inputs;
> - embedding:
> - `embed_dim` sets the dimension of embedding space;
> - `num_neg` sets the number of incorrect intent labels, the algorithm will minimize their similarity to the user input during training;
> - `similarity_type` sets the type of the similarity, it should be either `auto`, `cosine` or `inner`, if `auto`, it will be set depending on `loss_type`, `inner` for `softmax`, `cosine` for `margin`;
> - `loss_type` sets the type of the loss function, it should be either `softmax` or `margin`;
> - `ranking_length` defines the number of top confidences over which to normalize ranking results if `loss_type: "softmax"`; to turn off normalization set it to 0;
> - `mu_pos` controls how similar the algorithm should try to make embedding vectors for correct intent labels, used only if `loss_type` is set to `margin`;
> - `mu_neg` controls maximum negative similarity for incorrect intents, used only if `loss_type` is set to `margin`;
> - `use_max_sim_neg` if `true` the algorithm only minimizes maximum similarity over incorrect intent labels, used only if `loss_type` is set to `margin`;
> - `scale_loss` if `true` the algorithm will downscale the loss for examples where correct label is predicted with high confidence, used only if `loss_type` is set to `softmax`;
> - regularization:
> - `C2` sets the scale of L2 regularization;
> - `C_emb` sets the scale of how important is to minimize the maximum similarity between embeddings of different intent labels, used only if `loss_type` is set to `margin`;
> - `droprate_a` sets the dropout rate between layers before embedding layer for user inputs;
> - `droprate_b` sets the dropout rate between layers before embedding layer for system actions;

> - train accuracy calculation:
> - `evaluate_every_num_epochs` sets how often to calculate train accuracy, small values may hurt performance;
> - `evaluate_on_num_examples` how many examples to use for hold out validation set to calculate of validation accuracy, large values may hurt performance.
> 
> Warning
> Default `max_history` for this policy is `None` which means it’ll use the `FullDialogueTrackerFeaturizer`. We recommend to set `max_history` to some finite value in order to use `MaxHistoryTrackerFeaturizer` for **faster training**. See [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.7.2/api/core-featurization/#featurization-conversations) for details. We recommend to increase `batch_size` for `MaxHistoryTrackerFeaturizer` (e.g. `"batch_size": [32, 64]`)
> 
> Warning
> If `evaluate_on_num_examples` is non zero, random examples will be picked by stratified split and used as **hold out** validation set, so they will be excluded from training data. We suggest to set it to zero if data set contains a lot of unique examples of dialogue turns.
> 
> Note
> Droprate should be between `0` and `1`, e.g. `droprate=0.1` would drop out `10%` of input units.
> 
> Note
> For `cosine` similarity `mu_pos` and `mu_neg` should be between `-1` and `1`.
> 
> Note
> There is an option to use linearly increasing batch size. The idea comes from [https://arxiv.org/abs/1711.00489](https://arxiv.org/abs/1711.00489). In order to do it pass a list to `batch_size`, e.g. `"batch_size": [8, 32]` (default behaviour). If constant `batch_size` is required, pass an `int`, e.g. `"batch_size": 8`.
> 
> These parameters can be specified in the policy configuration file. The default values are defined in `EmbeddingPolicy.defaults`:
> 
> ```python
defaults = {
>     # nn architecture
>     # a list of hidden layers sizes before user embed layer
>     # number of hidden layers is equal to the length of this list
>     "hidden_layers_sizes_pre_dial": [],
>     # a list of hidden layers sizes before bot embed layer
>     # number of hidden layers is equal to the length of this list
>     "hidden_layers_sizes_bot": [],
>     # number of units in transformer
>     "transformer_size": 128,
>     # number of transformer layers
>     "num_transformer_layers": 1,
>     # type of positional encoding in transformer
>     "pos_encoding": "timing",  # string 'timing' or 'emb'
>     # max sequence length if pos_encoding='emb'
>     "max_seq_length": 256,
>     # number of attention heads in transformer
>     "num_heads": 4,
>     # training parameters
>     # initial and final batch sizes:
>     # batch size will be linearly increased for each epoch
>     "batch_size": [8, 32],
>     # how to create batches
>     "batch_strategy": "balanced",  # string 'sequence' or 'balanced'
>     # number of epochs
>     "epochs": 1,
>     # set random seed to any int to get reproducible results
>     "random_seed": None,
>     # embedding parameters
>     # dimension size of embedding vectors
>     "embed_dim": 20,
>     # the type of the similarity
>     "num_neg": 20,
>     # flag if minimize only maximum similarity over incorrect labels
>     "similarity_type": "auto",  # string 'auto' or 'cosine' or 'inner'
>     # the type of the loss function
>     "loss_type": "softmax",  # string 'softmax' or 'margin'
>     # number of top actions to normalize scores for softmax loss_type
>     # set to 0 to turn off normalization
>     "ranking_length": 10,
>     # how similar the algorithm should try
>     # to make embedding vectors for correct labels
>     "mu_pos": 0.8,  # should be 0.0 < ... < 1.0 for 'cosine'
>     # maximum negative similarity for incorrect labels
>     "mu_neg": -0.2,  # should be -1.0 < ... < 1.0 for 'cosine'
>     # the number of incorrect labels, the algorithm will minimize
>     # their similarity to the user input during training
>     "use_max_sim_neg": True,  # flag which loss function to use
>     # scale loss inverse proportionally to confidence of correct prediction
>     "scale_loss": True,
>     # regularization
>     # the scale of L2 regularization
>     "C2": 0.001,
>     # the scale of how important is to minimize the maximum similarity
>     # between embeddings of different labels
>     "C_emb": 0.8,
>     # dropout rate for dial nn
>     "droprate_a": 0.1,
>     # dropout rate for bot nn
>     "droprate_b": 0.0,
>     # visualization of accuracy
>     # how often calculate validation accuracy
>     "evaluate_every_num_epochs": 20,  # small values may hurt performance
>     # how many examples to use for hold out validation set
>     "evaluate_on_num_examples": 0,  # large values may hurt performance
> }
> ```
> 
> Note
> Parameter `mu_neg` is set to a negative value to mimic the original starspace algorithm in the case `mu_neg = mu_pos` and `use_max_sim_neg = False`. See [starspace paper](https://arxiv.org/abs/1709.03856) for details.

### 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
```

An intent can only be mapped to at most one action. The bot will run the mapped action once it receives a message of the triggering intent. Afterwards, it will listen for the next message. With the next user message, normal prediction will resume.

If you do not want your intent-action mapping to affect the dialogue history, the mapped action must return a `UserUtteranceReverted()` event. This will delete the user’s latest message, along with any events that happened after it, from the dialogue history. This means you should not include the intent-action interaction in your stories.

For example, if a user asks "Are you a bot?" off-topic in the middle of the flow, you probably want to answer without that interaction affecting the next action prediction. A triggered custom action can do anything, but here’s a simple example that dispatches a bot utterance and then reverts the interaction:

```python
class ActionIsBot(Action):
"""Revertible mapped action for utter_is_bot"""

def name(self):
    return "action_is_bot"

def run(self, dispatcher, tracker, domain):
    dispatcher.utter_template(template="utter_is_bot")
    return [UserUtteranceReverted()]
```

Note

If you use the `MappingPolicy` to predict bot utterance actions directly (e.g. `triggers: utter_{}`), these interactions must go in your stories, as in this case there is no `UserUtteranceReverted()` and the intent and the mapped response action will appear in the dialogue history.

Note

The MappingPolicy is also responsible for executing the default actions `action_back` and `action_restart` in response to `/back` and `/restart`. If it is not included in your policy example these intents will not work.

### 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`.

### Augmented Memoization Policy

The `AugmentedMemoizationPolicy` remembers examples from training stories for up to `max_history` turns, just like the `MemoizationPolicy`. Additionally, it has a forgetting mechanism that will forget a certain amount of steps in the conversation history and try to find a match in your stories with the reduced history. It predicts the next action with confidence `1.0` if a match is found, otherwise it predicts `None` with confidence `0.0`.

Note

If you have dialogues where some slots that are set during prediction time might not be set in training stories (e.g. in training stories starting with a reminder not all previous slots are set), make sure to add the relevant stories without slots to your training data as well.

### Fallback Policy

The `FallbackPolicy` invokes a [fallback action](https://legacy-docs-v1.rasa.com/1.7.2/core/fallback-actions/#fallback-actions) 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:**
> The thresholds and fallback action can be adjusted in the policy configuration file as parameters of the `FallbackPolicy`:
> 
> ```yaml
> policies:
>   - name: "FallbackPolicy"
>     nlu_threshold: 0.3
>     ambiguity_threshold: 0.1
>     core_threshold: 0.3
>     fallback_action_name: 'action_default_fallback'
> ```
> 
> |     |     |
> | --- | --- |
> | `nlu_threshold` | Min confidence needed to accept an NLU<br>prediction |
> | `ambiguity_threshold` | Min amount by which the confidence of the<br>top intent must exceed that of the second<br>highest ranked intent. |
> | `core_threshold` | Min confidence needed to accept an action<br>prediction from Rasa Core |
> | `fallback_action_name` | Name of the [fallback action](https://legacy-docs-v1.rasa.com/1.7.2/core/fallback-actions/#fallback-actions)<br>to be called if the confidence of intent<br>or action is below the respective threshold |
> 
> You can also configure the `FallbackPolicy` in your python code:
> ```python
> from rasa.core.policies.fallback import FallbackPolicy
> from rasa.core.policies.keras_policy import KerasPolicy
> from rasa.core.agent import Agent
> 
> fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
>                           core_threshold=0.3,
>                           nlu_threshold=0.3,
>                           ambiguity_threshold=0.1)
> 
> agent = Agent("domain.yml", policies=[KerasPolicy(), fallback])
> ```
> 
> Note
> You can include either the `FallbackPolicy` or the `TwoStageFallbackPolicy` in your configuration, but not both.

### Two-Stage Fallback Policy

The `TwoStageFallbackPolicy` handles low NLU confidence in multiple stages by trying to disambiguate the user input.

- If an NLU prediction has a low confidence score or is not significantly higher than the second highest ranked prediction, the user is asked to affirm the classification of the intent.
> - If they affirm, the story continues as if the intent was classified with high confidence from the beginning.
> - If they deny, the user is asked to rephrase their message.

- Rephrasing
> - If the classification of the rephrased intent was confident, the story continues as if the user had this intent from the beginning.
> - If the rephrased intent was not classified with high confidence, the user is asked to affirm the classified intent.

- Second affirmation
> - If the user affirms the intent, the story continues as if the user had this intent from the beginning.
> - If the user denies, the original intent is classified as the specified `deny_suggestion_intent_name`, and an ultimate fallback action is triggered (e.g. a handoff to a human).

**Configuration:**
> To use the `TwoStageFallbackPolicy`, include the following in your policy configuration.
> 
> ```yaml
> policies:
>   - name: TwoStageFallbackPolicy
>     nlu_threshold: 0.3
>     ambiguity_threshold: 0.1
>     core_threshold: 0.3
>     fallback_core_action_name: "action_default_fallback"
>     fallback_nlu_action_name: "action_default_fallback"
>     deny_suggestion_intent_name: "out_of_scope"
> ```
> 
> |     |     |
> | --- | --- |
> | `nlu_threshold` | Min confidence needed to accept an NLU<br>prediction |
> | `ambiguity_threshold` | Min amount by which the confidence of the<br>top intent must exceed that of the second<br>highest ranked intent. |
> | `core_threshold` | Min confidence needed to accept an action<br>prediction from Rasa Core |
> | `fallback_core_action_name` | Name of the [fallback action](https://legacy-docs-v1.rasa.com/1.7.2/core/fallback-actions/#fallback-actions)<br>to be called if the confidence of Rasa<br>Core action prediction is below the<br>`core_threshold`. This action is<br>to propose the recognized intents |
> | `fallback_nlu_action_name` | Name of the [fallback action](https://legacy-docs-v1.rasa.com/1.7.2/core/fallback-actions/#fallback-actions)<br>to be called if the confidence of Rasa<br>NLU intent classification is below the<br>`nlu_threshold`. This action is called<br>when the user denies the second time |
> | `deny_suggestion_intent_name` | The name of the intent which is used to<br>detect that the user denies the suggested<br>intents |
> 
> Note
> You can include either the `FallbackPolicy` or the `TwoStageFallbackPolicy` in your configuration, but not both.

### 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.