boyu-ai/Hands-on-RL
Hands On RL
Hands-on-RL is an interactive educational resource and collection of Jupyter notebooks designed for learning reinforcement learning. It combines technical theory with practical, runnable code to demonstrate the implementation and training of mainstream reinforcement learning agents.
The project focuses on bridging the gap between theory and practice through a tutorial structure that organizes explanations and executable code blocks sequentially. It enables the prototyping of reinforcement learning models to observe their behavior and performance in real-time.
The implementation utilizes a modular approach that separates agent logic from environment interactions. It employs tensor-based state representations and standardized environment wrappers to manage the interaction between agents and simulation environments.
Features
- Interactive Notebook Tutorials - Delivers educational content as interactive Jupyter notebooks with embedded executable code blocks.
- Deep Reinforcement Learning Implementations - Implements deep reinforcement learning algorithms to solve problems through a combination of theory and code.
- Agent Algorithm Implementations - Implements a modular architecture that separates agent logic from environment interactions for policy swapping.
- Reinforcement Learning - Implements and executes mainstream reinforcement learning algorithms within an interactive learning environment.
- Reinforcement Learning Curricula - Offers a structured curriculum for mastering reinforcement learning algorithms through theory and runnable examples.
- Jupyter Notebook Curricula - Organizes reinforcement learning education into structured learning paths delivered via Jupyter notebooks.
- AI & Machine Learning Education - Provides interactive education on machine learning concepts by bridging technical theory with practical code.
- Environment Wrappers - Provides standardized environment wrappers to preprocess simulation data for reinforcement learning agents.
- Tensor Data Representations - Uses multi-dimensional tensor representations for environmental inputs to enable efficient neural network processing.
- Algorithm Prototypes - Enables prototyping of reinforcement learning models to observe agent behavior and performance in real-time.
