Loading [MathJax]/extensions/MathMenu.js
Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management | IEEE Conference Publication | IEEE Xplore

Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management


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 det...Show More

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.
Date of Conference: 06-11 April 2025
Date Added to IEEE Xplore: 07 March 2025
ISBN Information:

ISSN Information:

Conference Location: Hyderabad, India
Figures are not available for this document.

Figures are not available for this document.

References

References is not available for this document.