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dist_classicrl
==============

A Python package for distributed classical reinforcement learning algorithms.

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🎯 **Project Description**
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**dist_classicrl** provides high-performance, scalable implementations of classic reinforcement learning algorithms with support for distributed training. The library focuses on Q-Learning with multiple execution modes: single-threaded, parallel (multiprocessing), and distributed (MPI) training.

🚀 **Key Features**
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- 🚀 **Multiple Execution Modes**: Single-threaded, parallel, and MPI-distributed training
- ⚡ **High Performance**: Optimized implementations with vectorized operations and performance benchmarking
- 🎮 **Multi-Agent Support**: Built-in support for multi-agent environments
- 🔧 **Flexible Architecture**: Abstract base classes for easy extension and custom environments
- 🌐 **Standards Compliant**: Compatible with Gymnasium and PettingZoo environments (coming soon)

📚 **Documentation**
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**Essential Guides:**

- 📦 `Installation Guide <docs/installation.rst>`_ - Setup instructions and requirements
- 📊 `Performance Benchmarks <docs/benchmarks.rst>`_ - Benchmarking results and analysis
- 📚 `Tutorials <docs/tutorials.rst>`_ - Step-by-step guides for using the library
- 🤝 `Contributing Guidelines <CONTRIBUTING.rst>`_ - Development standards and contribution process
- 📄 `License <LICENSE.txt>`_ - License terms and usage rights
- 👥 `Authors <AUTHORS.rst>`_ - Project contributors and maintainers
- 📜 `Changelog <CHANGELOG.rst>`_ - Project history and version changes
- 📜 `Code of Conduct <CODE_OF_CONDUCT.rst>`_ - Guidelines for participation and conduct

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