Table of Content
RLVR - Reinforcement Learning via Rust
"In theory, theory and practice are the same. In practice, they are not." — Albert Einstein
"RLVR - Reinforcement Learning via Rust" is a comprehensive guide that seamlessly integrates the foundational theories of reinforcement learning with practical implementation using the Rust programming language. Organized into four parts, the book begins with Part I: The Foundations, covering essential topics such as Introduction to Reinforcement Learning, Mathematical Foundations, Bandit Algorithms and Exploration-Exploitation Dilemmas, and Dynamic Programming in Reinforcement Learning. It then advances to Part II: The Algorithms, which delves into Monte Carlo Methods, Temporal-Difference Learning, Function Approximation Techniques, Eligibility Traces, Policy Gradient Methods, and Model-Based Reinforcement Learning. Part III: The Multi-Agents explores multi-agent reinforcement learning (MARL) through chapters on Introduction to Multi-Agent Systems, Game Theory for MARL, Learning in Multi-Agent Systems, and Foundational MARL Algorithms. The final section, Part IV: Deep RL Models, addresses advanced topics including Deep Learning Foundations, Deep Reinforcement Learning Models, Deep Hierarchical Reinforcement Learning, Multi-Agent Deep Reinforcement Learning, Federated Deep Reinforcement Learning, and Simulation Environments. Enhanced by hands-on projects and capstone examples, RLVR equips students, researchers, and professionals with the knowledge and tools to master reinforcement learning and make meaningful contributions using Rust, supported by insights from Stanford University's prominent CS234: Reinforcement Learning course.
Main Sections
Part I: The Foundations
- Chapter 1: Introduction to Reinforcement Learning
- Chapter 2: Mathematical Foundations of Reinforcement Learning
- Chapter 3: Bandit Algorithms and Exploration-Exploitation Dilemmas
- Chapter 4: Dynamic Programming in Reinforcement Learning
Part II: The Algorithms
- Chapter 5: Monte Carlo Methods
- Chapter 6: Temporal-Difference Learning
- Chapter 7: Function Approximation Techniques
- Chapter 8: Eligibility Traces
- Chapter 9: Policy Gradient Methods
- Chapter 10: Model-Based Reinforcement Learning
Part III: The Multi-Agents
- Chapter 11: Introduction to Multi-Agent Systems
- Chapter 12: Game Theory for MARL
- Chapter 13: Learning in Multi-Agent Systems
- Chapter 14: Foundational MARL Algorithms
Part IV: Deep RL Models
- Chapter 15: Deep Learning Foundations
- Chapter 16: Deep Reinforcement Learning Models
- Chapter 17: Deep Hierarchical Reinforcement Learning
- Chapter 18: Multi-Agent Deep Reinforcement Learning
- Chapter 19: Federated Deep Reinforcement Learning
- Chapter 20: Simulation Environments
Closing
Guidance for Readers
For Students 🎓
Embark on a comprehensive journey through reinforcement learning with a structured approach. From foundational concepts to advanced models, this book provides a clear path for students to build deep understanding and practical skills in RL using Rust.
For Lecturers 📚
A meticulously organized resource for designing RL curriculum. Offers comprehensive coverage, hands-on projects, and progressive learning modules that align seamlessly with academic teaching requirements.
For Researchers 🔬
Dive deep into advanced RL techniques, multi-agent systems, and cutting-edge deep learning approaches. A comprehensive resource for exploring and innovating in reinforcement learning research.