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Random Core issues when training the model, when using AugmentedMemoizationPolicy #8623

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@nico-sergeyssels-kbc

Description

Rasa version:
2.4.5

Python version:
3.7.9
Operating system (windows, osx, ...):
mac 10.15.7
Issue:

Normally when training this story AugmentedMemoizationPolicy should always kick in, we have noticed that after retraining and trying to do this story, it fails to use augmentation policy on the 3rd step once in a while. We checked the hashes and noticed that every few builds of the model these change for some reason.

Command used for training:

rasa train --augmentation 0

Content of configuration file (config.yml) (if relevant):

# Configuration for Rasa Core.
policies:
  - name: AugmentedMemoizationPolicy
    max_history: 6
  - name: TEDPolicy
    max_history: 6
    epochs: 30
  - name: FormPolicy
  - name: RulePolicy
    core_fallback_threshold: 0.3
    core_fallback_action_name: "action_fallback_core"
    enable_fallback_prediction: True
    check_for_contradictions: True

**Content of domain file (domain.yml)** (if relevant):
```yml
slot_one is a categorical one

training story

version: "2.0"
stories:
- story: example story
  steps:
  - intent: intent_one
  - action: action_one
  - slot_was_set:
    - slot_one: 1+
  - action: utter_one
  - intent: intent_two
    entities:
    - entity_one: entity_one_value
    - entity_two: entity_two_value
  - action: action_three

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area:rasa-oss 🎡Anything related to the open source Rasa frameworkarea:rasa-oss/ml 👁All issues related to machine learningarea:rasa-oss/ml/policiesIssues focused around rasa's dialogue management policiestype:bug 🐛Inconsistencies or issues which will cause an issue or problem for users or implementors.

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