CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
- URL: http://arxiv.org/abs/2501.17667v1
- Date: Wed, 29 Jan 2025 14:08:08 GMT
- Title: CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
- Authors: Derui Wang, Kristen Moore, Diksha Goel, Minjune Kim, Gang Li, Yang Li, Robin Doss, Minhui Xue, Bo Li, Seyit Camtepe, Liming Zhu,
- Abstract summary: Deep reinforcement learning (DRL) has gained widespread adoption in control and decision-making tasks due to its strong performance in dynamic environments.
Recent efforts have focused on addressing robustness issues by establishing rigorous theoretical guarantees for the returns achieved by DRL agents in adversarial settings.
We introduce a novel paradigm dubbed textttCertified-rtextttAdius-textttMaximizing textttPolicy (texttt CAMP) training to enhance DRL policies.
- Score: 27.55377940017779
- License:
- Abstract: Deep reinforcement learning (DRL) has gained widespread adoption in control and decision-making tasks due to its strong performance in dynamic environments. However, DRL agents are vulnerable to noisy observations and adversarial attacks, and concerns about the adversarial robustness of DRL systems have emerged. Recent efforts have focused on addressing these robustness issues by establishing rigorous theoretical guarantees for the returns achieved by DRL agents in adversarial settings. Among these approaches, policy smoothing has proven to be an effective and scalable method for certifying the robustness of DRL agents. Nevertheless, existing certifiably robust DRL relies on policies trained with simple Gaussian augmentations, resulting in a suboptimal trade-off between certified robustness and certified return. To address this issue, we introduce a novel paradigm dubbed \texttt{C}ertified-r\texttt{A}dius-\texttt{M}aximizing \texttt{P}olicy (\texttt{CAMP}) training. \texttt{CAMP} is designed to enhance DRL policies, achieving better utility without compromising provable robustness. By leveraging the insight that the global certified radius can be derived from local certified radii based on training-time statistics, \texttt{CAMP} formulates a surrogate loss related to the local certified radius and optimizes the policy guided by this surrogate loss. We also introduce \textit{policy imitation} as a novel technique to stabilize \texttt{CAMP} training. Experimental results demonstrate that \texttt{CAMP} significantly improves the robustness-return trade-off across various tasks. Based on the results, \texttt{CAMP} can achieve up to twice the certified expected return compared to that of baselines. Our code is available at https://github.com/NeuralSec/camp-robust-rl.
Related papers
- Improve Robustness of Reinforcement Learning against Observation
Perturbations via $l_\infty$ Lipschitz Policy Networks [8.39061976254379]
Deep Reinforcement Learning (DRL) has achieved remarkable advances in sequential decision tasks.
Recent works have revealed that DRL agents are susceptible to slight perturbations in observations.
We propose a novel robust reinforcement learning method called SortRL, which improves the robustness of DRL policies against observation perturbations.
arXiv Detail & Related papers (2023-12-14T08:57:22Z) - COPA: Certifying Robust Policies for Offline Reinforcement Learning
against Poisoning Attacks [49.15885037760725]
We focus on certifying the robustness of offline reinforcement learning (RL) in the presence of poisoning attacks.
We propose the first certification framework, COPA, to certify the number of poisoning trajectories that can be tolerated.
We prove that some of the proposed certification methods are theoretically tight and some are NP-Complete problems.
arXiv Detail & Related papers (2022-03-16T05:02:47Z) - Policy Smoothing for Provably Robust Reinforcement Learning [109.90239627115336]
We study the provable robustness of reinforcement learning against norm-bounded adversarial perturbations of the inputs.
We generate certificates that guarantee that the total reward obtained by the smoothed policy will not fall below a certain threshold under a norm-bounded adversarial of perturbation the input.
arXiv Detail & Related papers (2021-06-21T21:42:08Z) - CROP: Certifying Robust Policies for Reinforcement Learning through
Functional Smoothing [41.093241772796475]
We present the first framework of Certifying Robust Policies for reinforcement learning (CROP) against adversarial state perturbations.
We propose two types of robustness certification criteria: robustness of per-state actions and lower bound of cumulative rewards.
arXiv Detail & Related papers (2021-06-17T07:58:32Z) - Combining Pessimism with Optimism for Robust and Efficient Model-Based
Deep Reinforcement Learning [56.17667147101263]
In real-world tasks, reinforcement learning agents encounter situations that are not present during training time.
To ensure reliable performance, the RL agents need to exhibit robustness against worst-case situations.
We propose the Robust Hallucinated Upper-Confidence RL (RH-UCRL) algorithm to provably solve this problem.
arXiv Detail & Related papers (2021-03-18T16:50:17Z) - Robust Reinforcement Learning on State Observations with Learned Optimal
Adversary [86.0846119254031]
We study the robustness of reinforcement learning with adversarially perturbed state observations.
With a fixed agent policy, we demonstrate that an optimal adversary to perturb state observations can be found.
For DRL settings, this leads to a novel empirical adversarial attack to RL agents via a learned adversary that is much stronger than previous ones.
arXiv Detail & Related papers (2021-01-21T05:38:52Z) - Robust Deep Reinforcement Learning through Adversarial Loss [74.20501663956604]
Recent studies have shown that deep reinforcement learning agents are vulnerable to small adversarial perturbations on the agent's inputs.
We propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against adversarial attacks.
arXiv Detail & Related papers (2020-08-05T07:49:42Z) - Robust Deep Reinforcement Learning against Adversarial Perturbations on
State Observations [88.94162416324505]
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.
We show that naively applying existing techniques on improving robustness for classification tasks, like adversarial training, is ineffective for many RL tasks.
arXiv Detail & Related papers (2020-03-19T17:59:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.