mlsanityData to 14 Oct 2026

Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management

arXiv 2606.05526 ↗First posted 4 Jun 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

In this paper, multi-target tracking and scanning are considered in a radar system operating in the track-while-scan mode. Specifically, time allocation for radar scanning and tracking of multiple maneuvering targets under a time budget constraint is addressed, aiming to jointly optimize the performance of both tracking and scanning in a cognitive radar. We first present the details of the model for tracking and scanning and formulate the time management task as a constrained optimization problem. Subsequently, we design a glscdrl framework to find the time allocation strategy for the problem. In the proposed glscdrl framework, the parameters of the neural networks and the dual variable are learned simultaneously. The deep deterministic policy gradient (DDPG) algorithm is introduced to tackle continuous action space and its performance is compared with deep Q-learning, heuristic approaches, and an optimization-based approach. Numerical results show that the radar with the proposed glscdrl framework can autonomously allocate more time to the tracking task that requires greater attention while providing time for scanning and also constraining the total time budget below the predefined threshold.

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 12 of its 43 references that are in the map. First posted on arXiv 4 Jun 2026.

Builds on

12 of 43 references.

References · 12

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2017Constrained Policy Optimizationcited by 983
  4. 2018Reward Constrained Policy Optimizationcited by 417
  5. 2019Batch Policy Learning under Constraintscited by 244
  6. 2020Lagrangian Duality for Constrained Deep Learningcited by 74
  7. 2017OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real Worldcited by 70
  8. 2018Learning Optimal Resource Allocations in Wireless Systemscited by 40
  9. 2020Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environmentscited by 15
  10. 2020Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMAcited by 10
  11. 2020Quality of service based radar resource management using deep reinforcement learningcited by 3
  12. 2025Learning-Based Resource Management in Integrated Sensing and Communication Systemscited by 1

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  1. 2025Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management
  2. 2025Multi-Target Radar Search and Track Using Sequence-Capable Deep Reinforcement Learning
  3. 2024Online waveform selection for cognitive radar
  4. 2025A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
  5. 2022Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
  6. 2019Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences
  7. 2021Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications
  8. 2024An Index Policy Based on Sarsa and Q-learning for Heterogeneous Smart Target Tracking