Reinforcement Learning via Rust

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Reinforcement Learning via Rust: Mastering Algorithms, Multi-Agent Systems, and Deep RL Models by Jaisy Malikulmulki Arasy, Evan Pradipta Hardinatha, Chevan Walidain
English | December 24, 2024 | ISBN: N/A | ASIN: B0DRGJFQYD | 118 pages | PDF | 2.20 Mb
Reinforcement Learning via Rust (RLVR) is a comprehensive guide that bridges the gap between theoretical reinforcement learning concepts and their practical implementation using the Rust programming language. Structured into four parts, the book covers the essentials of reinforcement learning, from Markov Decision Processes and bandit algorithms to advanced techniques like Monte Carlo Methods, Temporal-Difference Learning, and Policy Gradient Methods. It also explores multi-agent reinforcement learning (MARL), game theory, and deep reinforcement learning models, including hierarchical approaches and federated learning. Each chapter is complemented by hands-on capstone projects and Generative AI prompts to reinforce learning. With a focus on real-world applications in robotics, business, healthcare, and natural language processing, this book equips readers with the knowledge to design, implement, and deploy reinforcement learning pipelines in Rust, making it an invaluable resource for students, professionals, and researchers seeking to master RL and its applications.



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