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..
Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness
Authors: Ezgi Korkmaz
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run multiple experiments in the Arcade Learning Environment (ALE) |
| Researcher Affiliation | Academia | The paper only lists the author name 'Ezgi Korkmaz' without any institutional affiliation or email domain. |
| Pseudocode | Yes | Algorithm 1: Probing Neural Manifold with High-sensitivity Directions within Perceptual Similarity |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of its own source code. |
| Open Datasets | Yes | The Arcade Learning Environment (Bellemare et al. 2013). |
| Dataset Splits | No | The paper states that results are from '10 independent runs' but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions several algorithms and frameworks like 'Deep Q-Network', 'prioritized experience replay', 'SA-DDQN', 'RADIAL', and 'Open AI Gym', but it does not specify version numbers for any of them. |
| Experiment Setup | No | The paper describes the general training methods and perturbation parameters (e.g., brightness/contrast values, rotation degrees) but does not provide specific hyperparameters for the deep reinforcement learning policy training itself, such as learning rate, batch size, or optimizer settings. |