Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management

• URL: http://arxiv.org/abs/2506.20853v1
• Date: Wed, 25 Jun 2025 21:56:30 GMT
• Title: Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management
• Authors: Ziyang Lu, Subodh Kalia, M. Cenk Gursoy, Chilukuri K. Mohan, Pramod K. Varshney,
• Abstract summary: We formulate this as a multi-objective optimization problem and employ deep reinforcement learning to find optimal solutions.<n>Our results demonstrate the effectiveness of both algorithms in adapting to various scenarios.<n>This work contributes to the development of more efficient and adaptive cognitive radar systems.
• Score: 13.322245764325125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The time allocation problem in multi-function cognitive radar systems focuses on the trade-off between scanning for newly emerging targets and tracking the previously detected targets. We formulate this as a multi-objective optimization problem and employ deep reinforcement learning to find Pareto-optimal solutions and compare deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) algorithms. Our results demonstrate the effectiveness of both algorithms in adapting to various scenarios, with SAC showing improved stability and sample efficiency compared to DDPG. We further employ the NSGA-II algorithm to estimate an upper bound on the Pareto front of the considered problem. This work contributes to the development of more efficient and adaptive cognitive radar systems capable of balancing multiple competing objectives in dynamic environments.

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