Evaluating Robustness of Cooperative MARL: A Model-based ApproachDownload PDF


22 Sept 2022, 12:37 (modified: 26 Oct 2022, 14:11)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: robust c-MARL, model-based adversarial attack
TL;DR: A novel model-based adversarial attack framework for cooperative multi-agent reinforcement learning with novel victim agent selection strategy.
Abstract: In recent years, a proliferation of methods were developed for cooperative multi-agent reinforcement learning (c-MARL). However, the robustness of c-MARL agents against adversarial attacks has been rarely explored. In this paper, we propose to evaluate the robustness of c-MARL agents via a model-based approach, named c-MBA. Our proposed formulation can craft much stronger adversarial state perturbations of c-MARL agents to lower total team rewards than existing model-free approaches. In addition, we propose the first victim-agent selection strategy and the first data-driven approach to define targeted failure states where each of them allows us to develop even stronger adversarial attack without the expert knowledge to the underlying environment. Our numerical experiments on two representative MARL benchmarks illustrate the advantage of our approach over other baselines: our model-based attack consistently outperforms other baselines in all tested environments.
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