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

arXiv: https://arxiv.org/abs/2211.06967 · HTML: https://mlsanity.com/paper/2211.06967/

## 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.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 2 of 2)

- [Multi-Agent Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1907.13220/) (2019)
- [Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences](https://mlsanity.com/paper/1912.00331/) (2019)

## Built on by (most cited first, 3 of 3)

- [Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing](https://mlsanity.com/paper/2409.14542/) (2024)
- [Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks](https://mlsanity.com/paper/2505.11461/) (2025)
- [Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approach](https://mlsanity.com/paper/2509.08956/) (2025)

## 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.