doc(FrozenLake_tuto): update policy exploitation logic to handle variable sets of maximum Q-values

[edelauna]

Description

Small change to the logic in the docs/tutorial/FrozenLake_tuto.py, the existing exploitation logic only randomly chooses an action if all actions have the same q-value. Whereas it's possible for a subset of actions to have a maximum q-value.

Updating the logic to retrieve an index of q-values which are equal to the max value, and then randomly selecting an action to take.

Fixes # (issue)

Type of change

Please delete options that are not relevant.

• Documentation only change (no code changed)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

Screenshots

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Checklist:

• I have run the pre-commit checks with pre-commit run --all-files (see CONTRIBUTING.md instructions to set it up)
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes

[pseudo-rnd-thoughts]

Personally, I understand the issue with just np.argmax but this solution looks good