mlsanityData to 14 Oct 2026

Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach

arXiv 2211.06967 ↗First posted 13 Nov 2022eess.SP cs.LGcited by 3

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

Consider a target being tracked by a cognitive radar network. If the target can intercept some radar network emissions, how can it detect coordination among the radars? By 'coordination' we mean that the radar emissions satisfy Pareto optimality with respect to multi-objective optimization over each radar's utility. This paper provides a novel multi-objective inverse reinforcement learning approach which allows for both detection of such Pareto optimal ('coordinating') behavior and subsequent reconstruction of each radar's utility function, given a finite dataset of radar network emissions. The method for accomplishing this is derived from the micro-economic setting of Revealed Preferences, and also applies to more general problems of inverse detection and learning of multi-objective optimizing systems.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 2 of its 16 references that are in the map. First posted on arXiv 13 Nov 2022.

Builds on

2 of 16 references.

Closest ancestors · 2

Same direction, newest first

  1. 2019Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences1 Dec
  2. 2019Multi-Agent Adversarial Inverse Reinforcement Learning30 Jul

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Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensingcited by 0
  2. 2025Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networkscited by 0
  3. 2025Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approachcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management
  2. 2022Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars
  3. 2026Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management
  4. 2025A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
  5. 2024Online waveform selection for cognitive radar
  6. 2022Adaptive ECCM for Mitigating Smart Jammers
  7. 2022How can a Radar Mask its Cognition?
  8. 2025Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning