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Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL
Yanchao Sun · Ruijie Zheng · Yongyuan Liang · Furong Huang

Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents. However, finding the optimal adversary is challenging, in terms of both whether we can find the optimal attack and how efficiently we can find it. Existing works on adversarial RL either use heuristics-based methods that may not find the strongest adversary, or directly train an RL-based adversary by treating the agent as a part of the environment, which can find the optimal adversary but may become intractable in a large state space. In this paper, we propose a novel attacking algorithm which has an RL-based director'' searching for the optimal policy perturbation, and anactor'' crafting state perturbations following the directions from the director (i.e. the actor executes targeted attacks). Our proposed algorithm, PA-AD, is theoretically optimal against an RL agent and significantly improves the efficiency compared with prior RL-based works in environments with large or pixel state spaces. Empirical results show that our proposed PA-AD universally outperforms state-of-the-art attacking methods in a wide range of environments. Our method can be easily applied to any RL algorithms to evaluate and improve their robustness.

#### Author Information

##### Furong Huang (University of Maryland)

Furong Huang is an assistant professor of computer science. Huang’s research focuses on machine learning, high-dimensional statistics and distributed algorithms—both the theoretical analysis and practical implementation of parallel spectral methods for latent variable graphical models. Some applications of her research include developing fast detection algorithms to discover hidden and overlapping user communities in social networks, learning convolutional sparse coding models for understanding semantic meanings of sentences and object recognition in images, healthcare analytics by learning a hierarchy on human diseases for guiding doctors to identify potential diseases afflicting patients, and more. Huang recently completed a postdoctoral position at Microsoft Research in New York.