Multi-Agent Search under Uncertainty: Reactive and Deep Q-Learning Methods

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 the 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 Pyth