✨ Add intermediate rewards with paper semantics

[flowerthrower]

🤖 AI text below 🤖

Description

Adds configurable intermediate rewards while preserving the existing PredictorEnv lifecycle and matching the paper prototype's reward behavior.

The public controls are intermediate_reward=True, reward_scale=1.0, and no_effect_penalty=-0.001. Setting intermediate_reward=False retains terminal-only rewards.

Comparable expected-fidelity and estimated-success-probability steps use exact or approximate figure-of-merit deltas. Terminal ESP uses ASAP scheduling and stops charging a qubit after its final measurement or reset. Critical-depth actions use exact non-terminal deltas, while estimated Hellinger distance remains neutral until termination. Structural progress and structural no-effect steps remain neutral; optimization no-effect and failed actions receive no_effect_penalty, and failures truncate the episode.

This is position 8 of the stack. It depends on #798 and is followed by #800. No new package dependencies are introduced.

Fixes #670

Checklist

• The pull request only contains commits that are focused and relevant to this change.
• I have added appropriate tests that cover the new/changed functionality.
• I have updated the documentation to reflect these changes.
• I have added entries to the changelog for any noteworthy additions, changes, fixes, or removals.
• I have added migration instructions to the upgrade guide (if needed).
• The changes follow the project's style guidelines and introduce no new warnings.
• The changes are fully tested and pass the CI checks.
• I have reviewed my own code changes.

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[codecov]

Codecov Report

❌ Patch coverage is 38.37838% with 114 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
src/mqt/predictor/rl/approx_reward.py	13.8%	81 Missing ⚠️
src/mqt/predictor/rl/predictorenv.py	28.2%	33 Missing ⚠️

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