Deep Reinforcement Learning for Robust Agent Systems
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Beschreibung
Deep Reinforcement Learning for Single-/Multi-Agent Systems provides a comprehensive guide to understanding and applying reinforcement learning (RL) in both single-agent and multi-agent contexts. This book is ideal for readers interested in mastering the fundamental concepts of RL and its advanced applications in real-world scenarios. Whether you’re a researcher, developer, or student, it offers a unique blend of theoretical depth and practical insights to empower you in tackling complex problems in AI and autonomous systems.
The book begins with foundational topics in RL, explaining key algorithms and methods for both single-agent and multi-agent systems. It then dives into robust reinforcement learning, focusing on adversarial attacks and defense techniques to improve model resilience in uncertain environments. The content also covers cutting-edge applications of RL, including the design of training environments, the deployment of RL in drone systems, and the integration of RL in large language models (LLMs).
By reading this book, you will gain valuable knowledge about state-of-the-art RL methodologies, learn to apply them in diverse settings, and understand how to defend against adversarial threats. The practical examples, case studies, and code snippets make it easier to implement RL solutions, while the in-depth discussions provide a solid foundation for further research. Prerequisite knowledge in machine learning and basic programming will be helpful, but the book is accessible to anyone with a keen interest in AI and reinforcement learning.
Deep Reinforcement Learning for Single-/Multi-Agent Systems provides a comprehensive guide to understanding and applying reinforcement learning (RL) in both single-agent and multi-agent contexts. This book is ideal for readers interested in mastering the fundamental concepts of RL and its advanced applications in real-world scenarios. Whether you’re a researcher, developer, or student, it offers a unique blend of theoretical depth and practical insights to empower you in tackling complex problems in AI and autonomous systems.
The book begins with foundational topics in RL, explaining key algorithms and methods for both single-agent and multi-agent systems. It then dives into robust reinforcement learning, focusing on adversarial attacks and defense techniques to improve model resilience in uncertain environments. The content also covers cutting-edge applications of RL, including the design of training environments, the deployment of RL in drone systems, and the integration of RL in large language models (LLMs).
By reading this book, you will gain valuable knowledge about state-of-the-art RL methodologies, learn to apply them in diverse settings, and understand how to defend against adversarial threats. The practical examples, case studies, and code snippets make it easier to implement RL solutions, while the in-depth discussions provide a solid foundation for further research. Prerequisite knowledge in machine learning and basic programming will be helpful, but the book is accessible to anyone with a keen interest in AI and reinforcement learning.
Covers both single-agent and multi-agent reinforcement learning from fundamentals to advanced methods Includes robust reinforcement learning with adversarial attacks, defence strategies, and real-world applications Presents theoretical foundations and algorithm pseudocode to support implementation and further research
Autor*in
Guanjun Liu
Themen in »Deep Reinforcement Learning for Robust Agent Systems«
Reinforcement Learning Deep Reinforcement Learning Multi-Agent Reinforcement Learning Adversarial Attacks in RL Robust Learning Stochastic Game Sim-to-Real Transfer Large Language Models