Solving the TSP via DRL

Table of Contents

1. Getting Started
   • Prerequisites
   • Installation
2. Usage
3. Contributing
4. License
5. Contact
6. Acknowledgments

About The Project

Reimplementation of the Policy Optimization with Multiple Optima framework of Kwon et al. Besides structural improvements, the repository also contains two extensions, called POMO-MCTS and POMO-AL.

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Getting Started

To get a local copy up and running follow these simple example steps.

Prerequisites

Installation

1. Clone the repo

   git clone https://github.com/dschaub95/rltsp.git

2. Create a conda environment

   conda env create -n ENVNAME --file environment.yml 

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Usage

Under construction.

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Darius Schaub - [email protected]

Project Link: https://github.com/dschaub95/rltsp

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Acknowledgments

• Kwon, Yeong-Dae, et al. "Pomo: Policy optimization with multiple optima for reinforcement learning." Advances in Neural Information Processing Systems 33 (2020): 21188-21198.

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