Add PR template adapted for EngiOpt

.github/PULL_REQUEST_TEMPLATE.md

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+# Description
+
+Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.
+
+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 algorithm (non-breaking change which adds a new model/algorithm)
+- [ ] Improvement to existing algorithm (non-breaking change which improves functionality)
+- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
+- [ ] This change requires a documentation update
+
+### Screenshots
+
+Please attach before and after screenshots of the change if applicable (e.g., generated designs, training curves, WandB dashboards).
+
+<!--
+Example:
+
+| Before | After |
+| ------ | ----- |
+| _gif/png before_ | _gif/png after_ |
+
+
+To upload images to a PR -- simply drag and drop an image while in edit mode and it should upload the image directly. You can then paste that source into the above before/after sections.
+-->
+
+# Checklist:
+
+### Code Quality
+- [ ] I have run the [`pre-commit` checks](https://pre-commit.com/) with `pre-commit run --all-files`
+- [ ] I have run `ruff check .` and `ruff format`
+- [ ] I have run `mypy .`
+- [ ] I have commented my code, particularly in hard-to-understand areas
+- [ ] My changes generate no new warnings
+
+### CleanRL Philosophy (for new/modified algorithms)
+- [ ] The implementation follows the [CleanRL](https://github.com/vwxyzjn/cleanrl) single-file philosophy: all training logic is contained in one file
+- [ ] The code is reproducible: random seeds are set, PyTorch determinism is enabled
+- [ ] Hyperparameters are configurable via command-line arguments using `tyro`
+- [ ] WandB logging is integrated with `--track` flag support
+- [ ] The model can be saved and restored via WandB artifacts (`--save-model` flag)
+
+### Algorithm Completeness (for new algorithms)
+- [ ] Both training script (`algorithm.py`) and evaluation script (`evaluate_algorithm.py`) are provided
+- [ ] The algorithm works with EngiBench's `Problem` interface
+- [ ] The algorithm is added to the README table with correct metadata
+
+### Documentation
+- [ ] I have made corresponding changes to the documentation
+- [ ] New algorithms include docstrings explaining the approach and any paper references
+
+<!--
+As you go through the checklist above, you can mark something as done by putting an x character in it
+
+For example,
+- [x] I have done this task
+- [ ] I have not done this task
+-->
+
+
+# Reviewer Checklist:
+
+- [ ] The content of this PR brings value to the community. It is not overly specific to a particular use case.
+- [ ] The tests and checks pass (linting, formatting, type checking).
+- [ ] The code follows the CleanRL single-file philosophy where appropriate.
+- [ ] The code is understandable and commented. No large code blocks are left unexplained. Can I read and understand the code easily?
+- [ ] There is no merge conflict.
+- [ ] For new algorithms: both training and evaluation scripts are provided.
+- [ ] For new algorithms: WandB integration is complete (logging, artifacts, reproducibility).
+- [ ] For bugfixes: it is a robust fix and not a hacky workaround.
+- [ ] The changes do not break existing trained models or evaluation workflows without good reason.