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
- 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
Policies
The rasa.core.policies.Policy class decides which action to take at every step in the conversation.
There are different policies:
- Configuring Policies
- Max History
- Data Augmentation
- Action Selection
- Keras Policy
- Embedding Policy
- TED Policy
- Mapping Policy
- Memoization Policy
- Augmented Memoization Policy
- Fallback Policy
- Two-Stage Fallback Policy
- Form Policy
Viewing: 1.9.4
Warning: This document is for an old version of Rasa. The latest version is 1.10.26.