Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
BadRL: Sparse Targeted Backdoor Attack against Reinforcement Learning
Authors: Jing Cui, Yufei Han, Yuzhe Ma, Jianbin Jiao, Junge Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on various classic RL tasks illustrate that Bad RL can substantially degrade the performance of a victim agent with minimal poisoning efforts (0.003% of total training steps) during training and infrequent attacks during testing. Code is available at: https://github.com/7777777cc/code. |
| Researcher Affiliation | Collaboration | Jing Cui1, Yufei Han2, Yuzhe Ma3, Jianbin Jiao1, Junge Zhang4,1* 1University of Chinese Academy of Sciences 2INRIA 3Microsoft Azure AI 4Institute of Automation, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1: Bad RL Algorithm |
| Open Source Code | Yes | Code is available at: https://github.com/7777777cc/code. |
| Open Datasets | Yes | Empirical results on various classic RL tasks illustrate that Bad RL can substantially degrade the performance of a victim agent... Empirical evaluations on four classic RL tasks reveal that Bad RL-based backdoor attacks... Pong, Breakout, Qbert, Space Invaders |
| Dataset Splits | No | The paper describes 'poisoning proportion: 0.003%, 0.003%, 0.002%, 0.002% for Pong, Breakout, Qbert, Space Invaders' as part of the training effort but does not specify dataset splits (e.g., train/validation/test percentages or counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions (e.g., Python version, PyTorch version, etc.) needed to replicate the experiment. |
| Experiment Setup | Yes | Poisoning proportion: 0.003%, 0.003%, 0.002%, 0.002% for Pong, Breakout, Qbert, Space Invaders. Models are tested every 10000 steps. |