Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing
<|reference_start|>Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing: 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.<|reference_end|>
@article{snow2024distributionally,
    title={Distributionally Robust Inverse Reinforcement Learning for Identifying
  Multi-Agent Coordinated Sensing},
    author={Luke Snow, Vikram Krishnamurthy},
    journal={arXiv preprint arXiv:2409.14542},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14542},
    primaryClass={cs.LG cs.MA eess.SP}
}