[Feature] Add RewardWithPenalties class and penalty functions

[Pabloo22]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @Pabloo22, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the library's ability to handle scheduling constraints by introducing a robust system for incorporating penalties into both metaheuristic objective functions and reinforcement learning reward mechanisms. It provides new classes and functions to calculate and apply penalties for deadline and due date violations, making the optimization and learning processes more sensitive to these critical constraints.

Highlights

• New Penalty Computation Functions: Introduced compute_penalty_for_deadlines and compute_penalty_for_due_dates in the metaheuristics module for modular and reusable penalty calculations related to schedule violations.
• Refactored Makespan Objective: The get_makespan_with_penalties_objective function now leverages the newly introduced dedicated penalty computation functions, enhancing code clarity and maintainability.
• RewardWithPenalties Class for RL: A new RewardObserver class, RewardWithPenalties, has been added to the reinforcement learning module. This class allows for the flexible combination of a base reward with custom penalties for constraint violations, such as deadlines and due dates.
• RL Penalty Utility Functions: Specific utility functions (get_deadline_violation_penalty, get_due_date_violation_penalty) were added to the reinforcement learning utilities for calculating penalties on a per-scheduled-operation basis, facilitating their use within the RewardWithPenalties class.
• Test File Renaming and Updates: The test file test_rl_utils.py was renamed to test_utils.py and updated to include comprehensive test cases for the newly added penalty utility functions, ensuring their correctness.

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[Copilot]

Pull Request Overview

This PR introduces a RewardWithPenalties class for the reinforcement learning module that adds penalty functionality to existing reward functions. The changes enable penalties for deadline and due date violations in job shop scheduling problems.

Key changes include:

• Added RewardWithPenalties class that wraps base reward observers with penalty functions
• Introduced penalty computation functions for deadline and due date violations
• Refactored metaheuristic objective functions to extract reusable penalty computation logic

Reviewed Changes

Copilot reviewed 7 out of 7 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
job_shop_lib/reinforcement_learning/_reward_observers.py	Adds the new RewardWithPenalties class that combines base rewards with penalty functions
job_shop_lib/reinforcement_learning/_utils.py	Introduces get_deadline_violation_penalty and get_due_date_violation_penalty functions
job_shop_lib/reinforcement_learning/__init__.py	Exports the new class and penalty functions in the module's public API
job_shop_lib/metaheuristics/_objective_functions.py	Refactors existing code to extract penalty computation into reusable functions
job_shop_lib/metaheuristics/__init__.py	Exports the new penalty computation functions
tests/reinforcement_learning/test_utils.py	Adds comprehensive tests for the penalty functions

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[gemini-code-assist]

Code Review

This pull request introduces a new RewardWithPenalties reward observer and associated penalty functions for deadline and due date violations. The implementation refactors existing penalty calculation logic from get_makespan_with_penalties_objective into separate, reusable functions. My review focuses on improving code maintainability by addressing duplication in the new penalty functions, correcting documentation, and highlighting a potential loss of test coverage from a removed file. I've also suggested adding type hints to a new test helper for better clarity.

[codecov-commenter]

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Codecov Report

✅ All modified and coverable lines are covered by tests.

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[Copilot]

Pull Request Overview

Copilot reviewed 9 out of 9 changed files in this pull request and generated 2 comments.

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[Copilot]

Pull Request Overview

Copilot reviewed 10 out of 10 changed files in this pull request and generated 1 comment.

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_{Tip: Customize your code reviews with copilot-instructions.md. Create the file or learn how to get started.}