mlsanityData to 14 Oct 2026

Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management

arXiv 2506.20853 ↗First posted 25 Jun 2025cs.LG eess.SPcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 15 references that are in the map. First posted on arXiv 25 Jun 2025.

Builds on

4 of 15 references.

References · 4

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2020Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environmentscited by 15
  4. 2020Quality of service based radar resource management using deep reinforcement learningcited by 3

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  1. 2026Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management
  2. 2022Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
  3. 2025A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
  4. 2024Online waveform selection for cognitive radar
  5. 2019Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences
  6. 2025Multi-Target Radar Search and Track Using Sequence-Capable Deep Reinforcement Learning
  7. 2025Learning-Based Resource Management in Integrated Sensing and Communication Systems
  8. 2022Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars