Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation
Keywords: Certified Learning, Adversarial Robustness, Formal Verification, Abstract Interpretation, Reinforcement Learning
TL;DR: A certifiably robust reinforcement learning algorithm through combination of model-based RL and abstract interpretation.
Abstract: We present a reinforcement learning (RL) framework in which the learned policy comes with a machine-checkable certificate of provable adversarial robustness. Our approach, called CAROL, learns a model of the environment. In each learning iteration, it uses the current version of this model and an external abstract interpreter to construct a differentiable signal for provable robustness. This signal is used to guide learning, and the abstract interpretation used to construct it directly leads to the robustness certificate returned at convergence. We give a theoretical analysis that bounds the worst-case accumulative reward of CAROL. We also experimentally evaluate CAROL on four MuJoCo environments with continuous state and action spaces. On these tasks, CAROL learns policies that, when contrasted with policies from two state-of-the-art robust RL algorithms, exhibit: (i) markedly enhanced certified performance lower bounds; and (ii) comparable performance under empirical adversarial attacks.
Submission Number: 22
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