Easy-RL is an educational resource designed to teach the principles and implementation of reinforcement learning. It provides a structured curriculum that guides users from fundamental concepts to advanced algorithmic techniques, focusing on the development and training of autonomous agents that learn through interaction with simulated environments.
The project distinguishes itself through a pedagogical framework that utilizes interactive notebooks to bridge the gap between theoretical research and functional code. By organizing complex methods into modular units, it allows for the study of individual agent components and the direct observation of training progress through integrated visual feedback tools.
The repository covers a broad range of machine learning capabilities, including the implementation of standard algorithms from scratch and the analysis of agent behavior in real-time. It serves as a comprehensive guide for mastering the mathematical foundations and practical deployment of decision-making models. All materials are provided as a collection of executable documents that combine explanatory text with hands-on coding exercises.
