Papers › Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

19 Mar 2020NeurIPS 2020 12arXiv:2003.08938archive 2025-07-28

Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane Boning, Cho-Jui Hsieh

A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises. Since the observations deviate from the true states, they can mislead the agent into making suboptimal actions. Several works have shown this vulnerability via adversarial attacks, but existing approaches on improving the robustness of DRL under this setting have limited success and lack for theoretical principles. We show that naively applying existing techniques on improving robustness for classification tasks, like adversarial training, is ineffective for many RL tasks. We propose the state-adversarial Markov decision process (SA-MDP) to study the fundamental properties of this problem, and develop a theoretically principled policy regularization which can be applied to a large family of DRL algorithms, including proximal policy optimization (PPO), deep deterministic policy gradient (DDPG) and deep Q networks (DQN), for both discrete and continuous action control problems. We significantly improve the robustness of PPO, DDPG and DQN agents under a suite of strong white box adversarial attacks, including new attacks of our own. Additionally, we find that a robust policy noticeably improves DRL performance even without an adversary in a number of environments. Our code is available at https://github.com/chenhongge/StateAdvDRL.

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chenhongge/StateAdvDRL officialmentioned in papermentioned on GitHubpaddle report

chenhongge/SA_DQN mentioned on GitHubpytorch report

huanzhang12/ATLA_robust_RL mentioned on GitHubpytorch report

huanzhang12/sa_ppo mentioned on GitHubpytorch report

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Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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Adam Batch Normalization Convolution DDPG DQN Dense Connections Entropy Regularization Experience Replay PPO Q-Learning ReLU Weight Decay

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