Reinforcement Learning Autonomous Systems (20 results)
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Architectures for Agentic AI: Integrating Multi-Agent Systems, Reinforcement Learning, and LLMs for Autonomous Decision-Making (SpringerBriefs in Intelligent Systems)
Oliveira, Pedro (Author)/ da Cruz Pereira, João (Author)/ Novais, Paulo (Author)
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Taschenbuch. Condition: Neu. Reinforcement Learning in Robotics and Autonomous Systems | The State of the Art | N. S. Usha (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208434175 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]prei…gu[dot]de | Anbieter: preigu.
Language: English
Published by Independently Published Mai 2026, 2026
Series: The Cognitive Spectrum Series, Book 3 of 3. Book 3 of 3 - The Cognitive Spectrum Series
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Taschenbuch. Condition: Neu. Neuware - Cognitive Electronic Warfare and Autonomous Spectrum Management - Volume IIIEdge AI, Distributed Electromagnetic Intelligence, and Autonomous Spectrum WarfareThe electromagnetic battlespace is becoming autonomous.As edge AI, distributed cognition, RFSoCs, and intelligent wireless systems co…nverge, the next generation of spectrum warfare is shifting from centralized control toward machine-speed autonomous coordination across contested electromagnetic environments.Volume III of The Cognitive Spectrum Series explores the execution layer of intelligent spectrum systems-where AI inference, SDR architectures, distributed decision systems, and real-time RF orchestration merge into adaptive electromagnetic ecosystems.Blending: - Edge AI inferencing, - RFSoC acceleration, - FPGA execution pipelines, - distributed cognitive networking, - adversarial machine learning, - autonomous EW architectures, - and real-time SDR orchestration, this volume presents a systems-engineering framework for understanding how future wireless systems will operate under extreme latency, bandwidth, synchronization, and adversarial constraints.Topics include: - TensorRT and ONNX Runtime- RFSoC and FPGA acceleration- Zero-copy SDR architectures- Shared-memory inference pipelines- Distributed spectrum intelligence- AI-native wireless orchestration- GNSS spoofing and navigation warfare- Cognitive mesh networking- Adversarial machine learning in RF- Autonomous EW coordination- Real-time scheduling systems- Edge AI for SDR platforms- Future 6G and THz systemsDesigned for: - RF engineers, - SDR developers, - FPGA architects, - AI researchers, - defense technologists, - edge-computing engineers, - and advanced wireless systems designers, this volume bridges the gap between theoretical AI systems and operational autonomous electromagnetic architectures.The future of spectrum warfare will not be manually controlled.It will be coordinated by intelligent machines operating at machine speed.
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Buch. Condition: Neu. Reinforcement Learning | Autonomous Systems, Ethical AI, Robotics | Madan Mohan Tito Ayyalasomayajula (u. a.) | Buch | XVIII | Englisch | 2026 | De Gruyter | EAN 9783111633596 | Verantwortliche Person für die EU: Walter de Gruyter GmbH, De Gruyter GmbH, Genthiner Str. 13, 10785 Berlin, productsafety[at]degr…uyterbrill[dot]com | Anbieter: preigu.
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Buch. Condition: Neu. Neuware - This book critically examines the ongoing transformation within Reinforcement Learning (RL), driven by significant advancements in computational power, algorithmic innovation, and interdisciplinary applications. As a vital branch of artificial intelligence, RL facilitates agents' learning through…interactions with their environment, increasingly underpinning the optimization of complex systems and enhancing decision-making capabilities. Its diverse applications, spanning autonomous vehicles, robotics, personalized recommendations, and financial trading, underscore RL's role as a foundational technology for future innovations. Given the pervasive integration of artificial intelligence across industries, a reimagining of RL is essential to address the multifaceted challenges posed by today's complex and dynamic environments. This work rigorously bridges theoretical developments with practical implementations, elucidating how RL can be harnessed to design adaptive systems capable of continuous improvement. By engaging with these emerging paradigms, this publication provides scholars and practitioners with the critical insights necessary to advance RL and AI, positioning them at the forefront of the next wave of technological progress.
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This cutting-edge technical book delves into the practical implementation of intelligent, volitional systems, offering a deep dive into the creation of autonomous agents. It explores the simulation of thought processes utilizing advanced concepts in both reinforcement learning and deep learning. An essential resource for researc…hers and practitioners interested in the frontiers of artificial intelligence and robotics. Note: The text is entirely in Japanese. Brand New Japanese Edition. Ships worldwide from Japan via Japan Post. Expedited shipping via FedEx available.
Language: English
Published by Independently published, 2026
Series: The Cognitive Spectrum Series, Book 3 of 3. Book 3 of 3 - The Cognitive Spectrum Series
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Language: English
Published by LAP LAMBERT Academic Publishing Mär 2025, 2025
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 100 pp. Englisch.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and… connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application.In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making.The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.
Language: English
Published by LAP LAMBERT Academic Publishing Mär 2025, 2025
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Reinforcement Learning (RL) has emerged as a transformative approach in the field of autonomous systems, enabling intelligent decision making and control in robotics, self-driving cars, healthcare, industrial automation, and smart infra…structure. Throughout this discussion, we have explored the fundamental concepts, methodologies, challenges, and real world applications of RL in autonomous systems, highlighting both its potential and its limitations. The application of RL in robotics and autonomous systems is underpinned by Markov Decision Processes (MDPs), which provide a structured framework for sequential decision making. The development of value based methods, such as Deep Q Networks (DQN), and policy-based approaches, such as Policy Gradient and Actor Critic methods, has enabled robots and autonomous agents to learn complex behaviors through trial and error. Moreover, model free and model based RL techniques offer different trade offs in terms of sample efficiency and adaptability, paving the way for more versatile and practical learning based controllers.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch.
