Demystifying Reinforcement Learning: A Mathematical Odyssey
Summary
Demystifying Reinforcement Learning: A Mathematical Odyssey' offers a comprehensive yet accessible exploration of the mathematical foundations underpinning reinforcement learning, guiding readers through fundamental concepts, algorithms, and techniques spanning value iteration, policy iteration, Monte Carlo methods, temporal-difference learning, value function approximation, policy gradient methods, and actor-critic methods.
Key Points
- This is a homepage for a new book titled 'Mathematical Foundations of Reinforcement Learning'
- The book aims to provide a mathematical but friendly introduction to fundamental concepts, problems, and algorithms in reinforcement learning
- It covers topics such as basic concepts, value iteration, policy iteration, Monte Carlo methods, temporal-difference learning, value function approximation, policy gradient methods, and actor-critic methods