End-to-End Differentiable Architecture: Structuring Deep Reinforcement Learning for Robotics Control

Name: End-to-End Differentiable Architecture: Structuring Deep Reinforcement Learning for Robotics Control
Brand: Independently Published
SKU: 9798346620174
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ISBN13: 9798346620174
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: November 13, 2024
Pages: 228
Original Price: GBP 31.4
Language: English
Edition: N/A
Item Weight: 309 grams
BISAC Subject(s): Robotics

About The Book

This comprehensive volume offers an in-depth exploration of end-to-end differentiable architectures in the context of deep reinforcement learning for robotics control. Serving as an essential resource for students, researchers, and practitioners in robotics and artificial intelligence, it systematically unpacks the complexities of designing and implementing sophisticated control policies for robotic systems.

Structured across 33 detailed chapters, the book begins with foundational concepts of deep reinforcement learning and progresses to advanced topics that address current challenges in the field. It delves into various neural network architectures suitable for control tasks, elucidates gradient-based learning methods, and examines both model-based and model-free reinforcement learning approaches. Readers will gain a thorough understanding of policy gradient methods, value-based methods like Q-learning, and optimization algorithms crucial for training effective control policies.

The text places significant emphasis on practical strategies for handling high-dimensional state and action spaces, managing the exploration-exploitation trade-offs, and designing robust reward functions. It also explores continuous action spaces, hierarchical reinforcement learning structures, and techniques for improving sample efficiency. Advanced chapters introduce cutting-edge topics such as incorporating attention mechanisms, memory-augmented neural networks, and uncertainty estimation into control architectures.

Readers will benefit from discussions on transfer learning, sim-to-real transfer techniques, and the integration of physical dynamics into learning architectures. The book also addresses the importance of regularization, generalization, and scalability in deep reinforcement learning methods. By integrating perception and control within a unified end-to-end differentiable framework, the text provides valuable insights into the future direction of robotics control.

Authored by experts in the field, this authoritative guide bridges the gap between theoretical foundations and practical applications, equipping readers with the knowledge and tools necessary to advance the capabilities of robotic control systems through deep reinforcement learning.

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