Algorithms for Reinforcement Learning

Comprehensive guide to reinforcement learning algorithms, covering dynamic programming, temporal difference, Monte-Carlo methods, and more. Suitable for researchers, students, and practitioners in AI, ML, and control engineering.

Introduction

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. This book focuses on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming, providing a comprehensive catalog of learning problems and describing the core ideas together with a large number of state-of-the-art algorithms, followed by the discussion of their theoretical properties and limitations.

Highlights

• Covers a wide range of reinforcement learning algorithms, including temporal difference learning, Monte-Carlo methods, function approximation techniques, and actor-critic methods
• Provides a thorough theoretical analysis of the algorithms, including their convergence properties and limitations
• Discusses practical applications of reinforcement learning in various domains

Recommendation

This course is recommended for anyone interested in reinforcement learning, including researchers, students, and practitioners in the fields of artificial intelligence, machine learning, and control engineering. It provides a solid foundation in the theory and practice of reinforcement learning, and is suitable for both beginners and advanced learners.