Reinforcement Learning Algorithms

Welcome to this comprehensive resource dedicated to introducing and discussing a wide array of reinforcement learning algorithms. Reinforcement learning is a powerful paradigm in artificial intelligence where an agent learns to make optimal decisions in an environment to maximize a cumulative reward.

Key Highlights

• Diverse Algorithm Coverage: I will delve into classic algorithms like Q-Learning, SARSA, and their extensions, as well as more advanced techniques such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO).
• In-depth Explanations: Each algorithm will be presented with detailed explanations of its underlying principles, mathematical formulations, and practical implementation aspects.
• Real-world Applications: Discover how these algorithms are applied in various fields, including robotics, game playing, autonomous vehicles, and resource management.

Look Inside

Jump to my full-fledged resource at https://erxiong0.github.io/chichi-gitbook to explore more in-depth content, code examples, and case studies related to reinforcement learning algorithms.