A Sensor Resource Scheduling Method and System Based on Combinatorial Action Space Reinforcement Learning
CN119809249BActive Publication Date: 2026-05-26NORTHWESTERN POLYTECHNICAL UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NORTHWESTERN POLYTECHNICAL UNIV
- Filing Date
- 2024-12-23
- Publication Date
- 2026-05-26
AI Technical Summary
Technical Problem
Existing single-agent reinforcement learning algorithms can only execute one discrete action, which cannot meet the scheduling requirements of multiple sensors for multiple flying targets.
Method used
A sensor resource scheduling method based on combined action space reinforcement learning is adopted. By constructing a DQN neural network and combining it with a Markov decision process, a sensor resource allocation model is trained to achieve the selection and optimal scheduling of multi-dimensional combined actions.
Benefits of technology
It achieves efficient scheduling of multiple flying targets, dynamically adjusts sensor resource allocation strategies, optimizes system performance, and reduces system energy consumption.
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Smart Images
Figure CN119809249B_ABST
Abstract
This invention belongs to the field of sensor resource allocation and discloses a sensor resource scheduling method and system based on reinforcement learning of combined action space. The method first obtains the system state of the sensor network system to be scheduled, then inputs the system state into a pre-trained sensor resource allocation model to obtain combined actions, and finally outputs the combined actions as the optimal scheduling scheme for flight targets. By designing a combined action space, this method overcomes the limitation of classical single-agent reinforcement learning algorithms that execute one discrete action at a time in discrete action space, enabling the execution of a multi-dimensional action combination at a given moment, and facilitating the scheduling of multiple sensors for tracking multiple flight targets. The sensor network system is trained using reinforcement learning to obtain a sensor scheduling scheme, enabling adaptive resource scheduling, dynamic adjustment of allocation strategies, and optimization of system performance.
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