Add single bid DRL strategy

[nick-harder]

Pull Request

Description

Adds a single-bid DRL strategy. To be merged after #574.

Proposed Changes

• Add a new single-bid DRL strategy for power plants to enable compatible simulations with storage units as well as the simulation of renewable energy generators
• Make the DRL bidding strategies' initialization more flexible by allowing changes to the default observation and action dimensions
• Refactor DRL strategy tests to use parametrization for improved modularity

Testing

Added a new test for the bidding strategy.

Checklist

Please check all applicable items:

• Code changes are sufficiently documented (docstrings, inline comments, doc folder updates)
• New unit tests added for new features or bug fixes
• Existing tests pass with the changes
• Reinforcement learning examples are operational (for DRL-related changes)
• Code tested with both local and Docker databases
• Code follows project style guidelines and best practices
• Changes are backwards compatible, or deprecation notices added
• New dependencies added to pyproject.toml
• A note for the release notes doc/release_notes.rst of the upcoming release is included
• Consent to release this PR's code under the GNU Affero General Public License v3.0

[Copilot]

Pull Request Overview

This PR adds a new single-bid DRL strategy, refactors existing DRL strategy tests to use parametrization, and updates the reinforcement learning algorithms to support dynamic agent counts and weight transfers.

• Introduces RLStrategySingleBid with a simplified bidding structure
• Refactors fixtures and tests in test_rl_strategies.py and adds extensive tests in test_matd3.py
• Updates learning strategy and algorithm modules for more flexible policy initialization and weight transfer

Reviewed Changes

Copilot reviewed 8 out of 9 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
tests/test_rl_strategies.py	Refactored tests to use parametrization and renamed fixtures for clarity
tests/test_matd3.py	Added extensive tests for saving, loading, and weight transfers in TD3
assume/strategies/learning_strategies.py	Introduced BaseLearningStrategy and RLStrategySingleBid, along with minor refactoring in RLStrategy
assume/scenario/loader_csv.py	Updated prompt messages and warning texts regarding overwriting policies
assume/reinforcement_learning/learning_utils.py	Added utility functions for hidden size extraction, agent inference, and weight transfer
assume/reinforcement_learning/learning_role.py	Modified exploration mode logic with a new loaded-only parameter
assume/reinforcement_learning/algorithms/matd3.py	Enhanced critic parameter loading to support agent count differences and weight transfers

Files not reviewed (1)

• docs/source/release_notes.rst: Language not supported

Comments suppressed due to low confidence (1)

assume/strategies/learning_strategies.py:195

• The variable name 'obd_dim' appears ambiguous and is likely a typo. Consider renaming it to 'obs_dim' for clarity and consistency with the rest of the code.

obd_dim = kwargs.pop("obs_dim", 38)

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 79.59%. Comparing base (c035460) to head (76d0c43).
Report is 6 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #576      +/-   ##
==========================================
+ Coverage   79.51%   79.59%   +0.07%     
==========================================
  Files          52       52              
  Lines        7342     7370      +28     
==========================================
+ Hits         5838     5866      +28     
  Misses       1504     1504              

Flag	Coverage Δ
pytest	79.59% <100.00%> (+0.07%)	⬆️

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[nick-harder]

@kim-mskw a review here would be really nice :-)