Foreword

In the dynamic and ever-evolving field of artificial intelligence, reinforcement learning (RL) has emerged as one of the most critical areas of study and application. As AI systems become increasingly sophisticated, the ability to learn from and adapt to environments in real-time has become paramount. Reinforcement learning lies at the heart of this capability, enabling machines to make decisions through trial and error, optimizing their actions to achieve specific goals. For students at Data Science Center Universitas Indonesia (DSC-UI) and beyond, gaining a deep understanding of RL is essential for staying at the forefront of AI advancements. RLVR - Reinforcement Learning via Rust is positioned as the baseline textbook for students at the DSC-UI, providing a comprehensive roadmap that not only covers foundational theories and practical implementations but also encourages continuous improvement and innovation. While Python has long been the dominant language in AI, particularly in reinforcement learning, Rust is gaining traction as an excellent choice for developing high-performance, reliable RL systems. Rust’s emphasis on memory safety, concurrency, and performance optimization offers unique advantages for building scalable and efficient AI models that meet the demanding requirements of real-world applications. As the field evolves, Rust’s innovative features and growing ecosystem make it an exciting and forward-thinking option for those eager to explore cutting-edge developments in AI.

At DSC-UI, we are dedicated to exploring Rust’s potential in reinforcement learning and artificial intelligence more broadly. By leveraging Rust’s emerging ecosystem, including its specialized crates for reinforcement learning, we aim to create systems that are robust, efficient, and capable of pushing the boundaries of what is possible. RLVR serves not only as a foundational text but also as a living resource that students can improve each semester by adding practical implementations of various use cases using Generative AI (GenAI). This collaborative approach fosters a vibrant learning environment where students can contribute to the book’s evolution, ensuring that the content remains current and highly relevant. For students and researchers, adopting Rust offers an opportunity to engage with a modern programming language that emphasizes precision and control—qualities that are invaluable in advanced RL implementations. Moreover, RLVR is designed to inspire students to pursue research topics in Deep Reinforcement Learning, providing the theoretical knowledge and practical skills necessary to undertake innovative projects and contribute to the advancement of the field.

Reinforcement learning is not just about programming—it is about understanding the mathematical and algorithmic principles that enable machines to learn and make decisions autonomously. This book, RLVR - Reinforcement Learning via Rust, offers a balanced approach, combining theoretical insights with practical guidance. It empowers learners to focus on core RL concepts while gaining hands-on experience in building and optimizing RL models using Rust. By bridging the gap between theory and practice, this book makes the study of reinforcement learning both accessible and engaging. Each chapter is structured to include Generative AI prompts and hands-on projects that facilitate deeper learning and practical application, making RLVR an indispensable resource for students, professionals, and researchers alike.

I hope that RLVR - Reinforcement Learning via Rust will inspire students at Universitas Indonesia and beyond, as well as professionals in related fields like mathematics, physics, and computer science. These disciplines are critical for advancing our understanding of AI, and with the right tools and knowledge, learners can find the journey of mastering reinforcement learning both rewarding and transformative. By serving as the baseline textbook for DSC-UI students, encouraging ongoing enhancements through practical GenAI implementations, and inspiring research in Deep Reinforcement Learning, RLVR is more than just a technical guide—it is an invitation to explore the future of AI through the lens of reinforcement learning, using Rust as a powerful tool to unlock new possibilities. I encourage students, educators, and AI enthusiasts to embrace this opportunity to learn, innovate, and contribute to the ever-evolving landscape of artificial intelligence.