LearningX provides a collection of Python examples for classical and deep reinforcement learning, alongside fundamental and advanced machine learning algorithms. It targets engineers and researchers seeking practical, runnable code to understand and implement various RL and ML concepts. The project offers a hands-on approach to learning through self-contained examples, each with its own detailed explanation.

How It Works

The project is structured as a series of independent Python scripts, each demonstrating a specific algorithm or concept. For reinforcement learning, it covers foundational methods like Q-Learning and advanced techniques such as Deep Q-Networks (DQN) applied to environments like Cartpole and Pong. Machine learning examples include supervised learning algorithms like decision trees and logistic regression, and unsupervised methods like K-means clustering, often implemented with gradient descent variants.

Quick Start & Requirements

• Install via pip install -r requirements.txt (assuming a requirements.txt file exists, though not explicitly stated in the README).
• Requires Python. Specific version not mentioned.
• OpenAI Gym is used for some RL examples.

Highlighted Details

• Covers both classical RL (Multi-Armed Bandit, Q-Learning) and deep RL (Cartpole, Pong).
• Includes basic ML (Classification, Regression) and unsupervised learning (K-means).
• Demonstrates advanced ML techniques like Tree Ensembles (Bagging, Boosting).

Maintenance & Community

• Maintained by Anson Wong.
• No community links (Discord, Slack, etc.) or roadmap are provided in the README.

Licensing & Compatibility

• License not specified in the README.
• Compatibility for commercial or closed-source use is unknown.

Limitations & Caveats

The README does not specify the Python version requirements, installation instructions beyond running main files, or licensing details, which may hinder quick adoption or commercial use. The project appears to be a personal collection of examples rather than a actively maintained library.