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Condizione: New.

Condizione: New. 2026. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Condizione: New. 2026. 1st Edition. hardcover. . . . . .

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condizione: new. Hardcover. Plan optimal multi-robot search paths despite imperfect sensor information When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation. The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice. Key topics include: Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditionsMulti-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasksDeep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviorsAlgorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readingsTheoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems development Designed for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. Plan optimal multi-robot search paths despite imperfect sensor information When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation. The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice. Key topics include: Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditionsMulti-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasksDeep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviorsAlgorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readingsTheoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems development Designed for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. Plan optimal multi-robot search paths despite imperfect sensor information When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation. The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice. Key topics include: Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditionsMulti-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasksDeep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviorsAlgorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readingsTheoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems development Designed for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…