datawhalechina/easy-rl

Features

• Structured Reinforcement Learning Curricula - Provides a structured collection of tutorials and code examples for mastering reinforcement learning.
• Interactive Notebook Curricula - Delivers an interactive curriculum using executable notebooks to visualize and experiment with reinforcement learning.
• Machine Learning Guides - Serves as a comprehensive educational guide for developers and students to implement intelligent agents.
• Reinforcement Learning Algorithms - Implements methods for training agents to make sequences of decisions to maximize cumulative rewards.
• Algorithm Implementations - Provides clean, readable educational implementations of standard reinforcement learning algorithms.
• Interactive Notebooks - Combines executable code, narrative text, and visualizations for educational experimentation.
• Autonomous Agents - Builds autonomous agents capable of making decisions independently using reinforcement learning.
• Reinforcement Learning Performance Visualizers - Analyzes reinforcement learning agent performance through graphical plots and animations in interactive notebooks.
• Agent Performance Visualizers - Renders agent trajectories and policies to analyze reinforcement learning model performance in real-time.
• Reinforcement Learning Environments - Provides standardized interfaces for defining state, action, and reward logic to train autonomous agents.
• Progression and Sequencing Systems - Enforces logical ordering of learning tasks to guide user advancement through reinforcement learning concepts.

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