# Policies

> Warning: This document is for an old version of Rasa. The latest version is 1.10.26.

## Configuring Policies

The `rasa.core.policies.Policy` class decides which action to take at every step in the conversation.

### 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.

### 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.

### 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 to select the next action.

### TED Policy

The Transformer Embedding Dialogue (TED) Policy is a model for dialogue management.

### Mapping Policy

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

### Memoization Policy

The `MemoizationPolicy` just memorizes the conversations in your training data.

### Augmented Memoization Policy

The `AugmentedMemoizationPolicy` remembers examples from training stories for up to `max_history` turns.

### Fallback Policy

The `FallbackPolicy` invokes a fallback action if certain confidence thresholds are not met.

### 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 manages form filling.  
  
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