mlsanityData to 14 Oct 2026

Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing

arXiv 2409.14542 ↗First posted 22 Sep 2024cs.LG cs.MA eess.SPcited by 0

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

Abstract (arXiv)

We derive a minimax distributionally robust inverse reinforcement learning (IRL) algorithm to reconstruct the utility functions of a multi-agent sensing system. Specifically, we construct utility estimators which minimize the worst-case prediction error over a Wasserstein ambiguity set centered at noisy signal observations. We prove the equivalence between this robust estimation and a semi-infinite optimization reformulation, and we propose a consistent algorithm to compute solutions. We illustrate the efficacy of this robust IRL scheme in numerical studies to reconstruct the utility functions of a cognitive radar network from observed tracking signals.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 21 references that are in the map. First posted on arXiv 22 Sep 2024.

Builds on

9 of 21 references.

Closest ancestors · 4

Same direction, newest first

  1. 2022Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach13 Nov
  2. 2022Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars3 May
  3. 2019Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences1 Dec
  4. 2019Multi-Agent Adversarial Inverse Reinforcement Learning30 Jul

Other references · 5

  1. 2020Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interferencecited by 10
  2. 2017Decomposition Algorithm for Distributionally Robust Optimization using Wasserstein Metriccited by 4
  3. 2020Wasserstein Distributionally Robust Inverse Multiobjective Optimizationcited by 4
  4. 2023Statistical Detection of Coordination in a Cognitive Radar Network through Inverse Multi-objective Optimizationcited by 2
  5. 2024Data-Driven Mechanism Design via Multi-Agent Revealed Preferencescited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approach
  2. 2025Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization
  3. 2025Distributional Inverse Reinforcement Learning
  4. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  5. 2023A Novel Variational Lower Bound for Inverse Reinforcement Learning
  6. 2024On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning
  7. 2025Inverse Reinforcement Learning via Convex Optimization
  8. 2022Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner