Understanding the decision-maker of your bot (policies)

The policy method learns from stories and predicts the next actions.

Policies need to use a featurizer to convert stories into conversational states, get the state features, and use those features to predict the next action.

In Rasa, we can have multiple policies. Policies are trained independently and can be used together for final prediction according to their priorities and confidence scores.

Let's start with policy configuration.

Configuring policies

Policy configuration is done in the config.yaml file within a Rasa project. The part with key policies is reserved for policy configuration. Here is an example:

policies:
  - name: "MemoizationPolicy"
    max_history: 5
  - name: "FallbackPolicy"
    nlu_threshold: 0.4
    core_threshold: 0.3
    fallback_action_name: "my_fallback_action...