Natural Black-Box Adversarial Examples against Deep Reinforcement Learning
Authors: Mengran Yu, Shiliang Sun8936-8944
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on multiple environments demonstrate the effectiveness of adv RL-GAN in terms of reward reductions and magnitudes of perturbations, and validate the sparse and targeted property of adversarial perturbations through visualization. |
| Researcher Affiliation | Academia | Mengran Yu and Shiliang Sun* School of Computer Science and Technology, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, P.R. China mengranyu97@gmail.com, slsun@cs.ecnu.edu.cn |
| Pseudocode | Yes | Algorithm 1: Adv RL-GAN Attack |
| Open Source Code | No | The paper does not provide any concrete access information for source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | We evaluate the performance of the proposed adv RL-GAN attack against DRL agents on 6 different Atari 2600 games, including Breakout, Pong, Chopper Command, Space Invaders, Ms Pacman, and Qbert, using Open AI Gym (Brockman et al. 2016). |
| Dataset Splits | No | The paper mentions training target policies but does not provide specific details on training, validation, and test dataset splits, percentages, or sample counts. |
| Hardware Specification | Yes | Experiments are performed with the Py Torch library on Ge Force RTX 2080Ti GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch library' but does not provide a specific version number, nor does it list versions for other software dependencies. |
| Experiment Setup | Yes | We train the adv RL-GAN model with 8 × 10^5 steps by adopting the REINFORCE algorithm. The hyper-parameters of the loss function LG are set to λ1 = 10, λ2 = 0.1, and λ3 = 10. |