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

• URL: http://arxiv.org/abs/2409.14542v1
• Date: Sun, 22 Sep 2024 17:44:32 GMT
• Title: Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing
• Authors: Luke Snow, Vikram Krishnamurthy,
• Abstract summary: We derive a minimax distributionally robust inverse reinforcement learning (IRL) algorithm to reconstruct the utility functions of a multi-agent sensing system. We prove the equivalence between this robust estimation and a semi-infinite optimization reformulation, and we propose a consistent algorithm to compute solutions.
• Score: 13.440621354486906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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