Foreword

As the Head of the Data Science Center (DSC) at the Universitas Indonesia, I have closely observed the remarkable strides made in artificial intelligence, particularly in the field of reinforcement learning (RL). Reinforcement learning has become a cornerstone of AI, enabling machines to learn from their environments and make decisions that maximize cumulative rewards. This paradigm shift is reshaping industries and academia alike, as RL techniques are increasingly applied to solve complex real-world problems. However, as you embark on your journey to master RL, it is essential to recognize that the true power of these algorithms lies in the foundational mathematical concepts that underpin them, coupled with the selection of the right tools and frameworks for implementation.

For students in the Mathematics faculty, understanding the mathematics behind RL is not merely important—it is vital. Linear algebra, calculus, probability, and dynamic programming form the backbone of RL algorithms. These mathematical principles are what allow RL models to function effectively, enabling agents to learn and adapt in uncertain and dynamic environments. Mastering these concepts is key to unlocking the full potential of RL. But beyond the mathematical foundation, the choice of programming language and tools plays a critical role in how these models are implemented, optimized, and deployed in real-world scenarios.

In the realm of AI, Rust has emerged as an exceptional companion for developing the low-level systems that power RL applications. Unlike more widely adopted languages like Python, Rust offers unparalleled advantages in terms of memory safety, concurrency, and performance optimization. These features are particularly crucial when building and deploying RL models at scale, where efficiency, reliability, and robustness are paramount. Rust’s growing ecosystem, with powerful crates like burn and others specifically designed for RL, provides the necessary tools to implement sophisticated algorithms while ensuring that the underlying systems are both efficient and resilient.

However, it is important to remember that tools and frameworks are only as effective as the mathematical understanding that drives their use. Rust’s capabilities in managing low-level system complexities make it an ideal choice for those who are serious about advancing the field of RL. Yet, to truly leverage this power, one must first have a deep comprehension of the mathematical abstractions and algorithmic intricacies that define reinforcement learning.

This book, "RLVR - Reinforcement Learning via Rust," is designed to guide you through both the theoretical and practical aspects of reinforcement learning. It emphasizes the importance of a strong mathematical foundation while introducing you to the practicalities of implementing RL models using Rust. By doing so, it bridges the gap between high-level mathematical concepts and low-level system implementation, offering a comprehensive resource for those looking to excel in this field.

I encourage you to approach this book with a deep appreciation for both the mathematics and the technology that make RL possible. Rust is more than just a tool; it is a language that allows you to build efficient, reliable, and scalable systems that are at the core of modern AI applications. As you progress through this book, let it inspire you to not only learn but also innovate and contribute to the future of AI technology.

With a strong mathematical foundation and Rust as your companion, you are well-equipped to navigate the complexities of RL development and make meaningful contributions to this exciting and rapidly evolving field.