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CONTRIBUTING.md

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Future Directions

  • Addressing single-agent non-stationarity issues by leveraging multi-agent DRL.
  • Accelerating the learning of optimal offloading policies by taking advantage of Federated Learning techniques in the training process. This will allow MDs to collectively contribute to improving the offloading model and enable continuous learning when new MDs join the network.
  • Addressing partially observable environment issues by designing a decentralized Partially Observable Markov Decision Process (Dec-POMDP).
  • Extending the Task Models by considering interdependencies among tasks. This can be achieved by incorporating a Task Call Graph Representation.
  • Implementation of the D3QN algorithm using PyTorch, focusing on efficient parallelization and enhanced model stability.

Contributing

We welcome contributions! Here’s how you can get involved:

  1. Fork the repository: Create your own copy of the project.
  2. Clone Your Fork:
  git clone https://github.com/<your-username>/<repo-name>.git  
  cd <repo-name>
  1. Create a new branch: Name your branch to reflect the changes you're making.
  git checkout -b feature/<add-future-direction-support>
  1. Commit your changes: Write clear and concise commit messages.
  git add * 
  git commit -a -m "<add-future-direction-support>"
  1. Push your branch:
  git push origin feature/<add-future-direction-support>
  1. Open a pull request: Navigate to the repository and submit your pull request. Provide a detailed description of your work.

For bug reports or feature requests, open a GitHub issue here.