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Add initial project structure and implement core functionality
- Create issue templates for bug reports and feature requests. - Add a pull request template with a checklist. - Set up CI workflow with GitHub Actions for testing across multiple Python versions. - Configure pre-commit hooks with Ruff for linting and formatting. - Document project changes in a changelog. - Establish a code of conduct and contributing guidelines. - Define a security policy for vulnerability reporting. - Set up project metadata in pyproject.toml, including dependencies and scripts. - Implement the Space Debris Avoidance environment using Gymnasium. - Develop CLI for training and evaluating reinforcement learning models. - Create a self-healing service simulator with anomaly detection. - Add smoke tests for CLI help and package imports.
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‎.github/ISSUE_TEMPLATE/bug_report.yml‎

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name: Bug report
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description: Report a bug
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labels: [bug]
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body:
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- type: textarea
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id: what
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attributes:
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label: What happened?
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description: What did you expect to happen?
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validations:
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required: true
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- type: textarea
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id: repro
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attributes:
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label: Steps to reproduce
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placeholder: |
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1.
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2.
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3.
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validations:
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required: true
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- type: input
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id: python
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attributes:
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label: Python version
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placeholder: e.g. 3.11.8
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- type: textarea
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id: logs
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attributes:
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label: Logs / tracebacks
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render: shell

‎.github/ISSUE_TEMPLATE/feature_request.yml‎

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name: Feature request
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description: Suggest an idea
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labels: [enhancement]
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body:
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- type: textarea
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id: proposal
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attributes:
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label: Proposal
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description: What would you like to see added/changed?
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validations:
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required: true
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- type: textarea
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id: context
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attributes:
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label: Context
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description: Why is this useful?

‎.github/pull_request_template.md‎

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## Summary
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-
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## Checklist
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- [ ] Tests added/updated (if applicable)
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- [ ] `ruff check .` passes
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- [ ] `ruff format --check .` passes
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- [ ] `pytest` passes

‎.github/workflows/ci.yml‎

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name: CI
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on:
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push:
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branches: [main]
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pull_request:
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.10", "3.11", "3.12"]
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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cache: pip
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- name: Install
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run: |
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python -m pip install --upgrade pip
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pip install -e ".[dev]"
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- name: Lint
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run: |
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ruff check .
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ruff format --check .
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- name: Type check
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run: pyright
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- name: Tests
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run: pytest -q

‎.pre-commit-config.yaml‎

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repos:
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.5.7
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hooks:
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- id: ruff
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args: [--fix]
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- id: ruff-format

‎CHANGELOG.md‎

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# Changelog
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All notable changes to this project will be documented in this file.
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## 0.1.0
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- Package the demos under `src/space_debris_rl/`.
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- Add CLI entry points: `space-debris-rl` and `service-self-healing`.
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- Add minimal pytest smoke tests and GitHub Actions CI.
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- Add Ruff + pre-commit configuration.
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- Add governance and security documentation.

‎CODE_OF_CONDUCT.md‎

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# Code of Conduct
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This project follows the Contributor Covenant Code of Conduct.
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## Our Pledge
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We pledge to make participation in our community a harassment-free experience for everyone.
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## Our Standards
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Examples of behavior that contributes to a positive environment include:
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- Using welcoming and inclusive language
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- Being respectful of differing viewpoints
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- Gracefully accepting constructive criticism
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## Enforcement
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Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the maintainers.
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For the full text, see: https://www.contributor-covenant.org/version/2/1/code_of_conduct/

‎CONTRIBUTING.md‎

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# Contributing
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Thanks for taking the time to contribute.
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## Development setup
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- Create a virtualenv and install dev deps:
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- `pip install -e ".[dev]"`
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- Run quality checks:
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- `ruff check .`
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- `ruff format .`
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- `pytest`
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## Pull requests
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- Keep PRs focused and small.
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- Add/adjust tests when changing behavior.
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- Ensure CI is green.
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## Code style
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- Formatting/linting is enforced via Ruff.
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- Prefer small, readable functions and explicit error messages.

‎README.md‎

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# Space-Debris-RL-Python-Script-
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Reinforcement Learning enables satellites to learn optimal maneuvers by trial and error, receiving rewards for avoiding collisions and penalties for risky moves. Applied to constellations like Starlink, AI can minimize fuel use while safely dodging space debris in real time.
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# Space Debris RL
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## RL demo: space debris collision avoidance
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Reinforcement Learning enables satellites to learn optimal maneuvers by trial and error, receiving rewards for avoiding collisions and penalties for risky moves. This repo packages two demos as an installable Python project:
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The main RL demo is implemented in `space_debris_rl.py`. It defines a custom (Gym/Gymnasium) environment and trains a PPO agent (Stable-Baselines3) to reach a goal while avoiding moving debris.
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- **RL demo:** 2D space-debris collision avoidance with PPO
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- **Self-healing demo:** anomaly detection + automated recovery loop simulator
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**Install (typical)**
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## Quickstart (RL demo)
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pip install gymnasium numpy matplotlib stable-baselines3 torch
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### 1) Create a virtualenv
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Note: `torch`/`stable-baselines3` wheels may not be available for very new Python versions yet. If installation fails, try Python 3.10–3.12.
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Windows (PowerShell):
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**Run**
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python -m venv .venv
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.\.venv\Scripts\Activate.ps1
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python space_debris_rl.py
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### 2) Install
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### What happens
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python -m pip install --upgrade pip
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pip install -e ".[rl]"
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- The agent is trained for **100,000** time steps (this may take a few minutes on a CPU).
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- After training, **five evaluation episodes** are shown in a Matplotlib window.
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- Each episode displays the spacecraft (blue dot), debris (red dots), and goal (green star). The agent attempts to reach the goal while avoiding collisions.
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### 3) Run
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### Customisation
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space-debris-rl run
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- Number of debris: change `self.num_debris` in the environment.
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- Thrust strength: adjust `self.thrust`.
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- Goal position: modify `self.goal_pos`.
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- Training duration: increase `total_timesteps` in `train()` / `model.learn()` for better performance.
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- RL algorithm: replace PPO with DQN, A2C, etc., if desired.
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What happens:
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This code is intentionally simplified for demonstration. Real-world space debris avoidance would involve 3D dynamics, more precise orbit propagation, sensor noise, and a larger action space.
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- Trains for **100,000** timesteps by default.
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- Evaluates **5 episodes** and renders trajectories in a Matplotlib window.
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## Training vs evaluation (inference)
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Train and save a model (Stable-Baselines3 saves `*.zip` automatically):
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space-debris-rl train --timesteps 100000 --model space_debris_ppo
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Evaluate a saved model:
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space-debris-rl evaluate --model space_debris_ppo.zip --episodes 5
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Tip: use `--no-render` on headless machines.
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## Quickstart (self-healing demo)
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pip install -e ".[self-healing]"
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service-self-healing
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## Product‑Ready: key development areas
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This repo also includes a standalone anomaly-detection + auto-repair simulation.
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**Install**
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python service_self_healing.py
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This demo is now exposed via the `service-self-healing` CLI.
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### Next sprint (product-ready)
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| Validation | Fault-injection harness + summary report of results |
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Out of scope for this sprint: full online learning, deep root-cause analysis, and production integrations (Prometheus/Grafana/PagerDuty) beyond simple adapters.
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## Development
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Install dev tooling:
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pip install -e ".[dev]"
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Run checks:
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ruff check .
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ruff format --check .
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pytest
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Enable pre-commit:
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pre-commit install
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## Model artifact storage policy
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The example trained model is stored as `space_debris_ppo.zip`.
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For a "product" workflow, it’s usually better to store large artifacts as:
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- a GitHub Release asset, or
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- Git LFS, or
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- downloaded at runtime (with a checksum).
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If you distribute a model file, consider publishing a SHA-256 checksum. On Windows:
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certutil -hashfile space_debris_ppo.zip SHA256

‎SECURITY.md‎

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# Security Policy
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## Supported Versions
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This project is a demo repository. Security fixes are provided on a best-effort basis.
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## Reporting a Vulnerability
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Please **do not** open public issues for suspected vulnerabilities.
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- Email the maintainer(s) with a clear description and reproduction steps.
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- If you cannot find a security contact, open a GitHub issue with minimal details and request a private channel.
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We will acknowledge receipt and coordinate a fix/release when appropriate.

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