Policies
These docs are for version 1.x of Rasa Open Source.
User Guide
- Installation
- Tutorial: Rasa Basics
- Tutorial: Building Assistants
- Command Line Interface
- Architecture
- Messaging and Voice Channels
- Testing Your Assistant
- Setting up CI/CD
- Validate Data
- Configuring the HTTP API
- Deploying Your Rasa Assistant
- Cloud Storage
NLU
- About
- Using NLU Only
- Training Data Format
- Language Support
- Choosing a Pipeline
- Components
- Entity Extraction
Core
- About
- Stories
- Domains
- Responses
- Actions
- Reminders and External Events
- Policies
- Slots
- Forms
- Retrieval Actions
- Interactive Learning
- Fallback Actions
- Knowledge Base Actions
Conversation Design
API Reference
- Action Server
- HTTP API
- Jupyter Notebooks
- Agent
- Custom NLU Components
- Rasa SDK
- Events
- Tracker
- Tracker Stores
- Event Brokers
- Lock Stores
- Training Data Importers
- Featurization of Conversations
- TensorFlow Configuration
- Migration Guide
- Rasa Open Source Change Log
Migrate from (beta)
Reference
Versions
viewing: 1.10.0
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.
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.
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. The bot’s next action is then decided by the policy that predicts with the highest confidence.
Keras Policy
The KerasPolicy uses a neural network implemented in Keras to select the next action. The default architecture is based on an LSTM.
Embedding Policy
Warning
EmbeddingPolicywas renamed toTEDPolicy. Please use TED Policy instead ofEmbeddingPolicyin your policy configuration. The functionality of the policy stayed the same.
TED Policy
The Transformer Embedding Dialogue (TED) Policy is described in our paper. This policy has a pre-defined architecture.
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 at least one of the following occurs: The intent recognition has a confidence below nlu_threshold.
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 handles the filling of forms.