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..
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
Authors: Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform numerical simulations on an environment represented as an MDP with four states and two actions, see Figure 2 for details. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Software Systems (MPI-SWS). 2University of Wisconsin-Madison. |
| Pseudocode | No | The paper describes its methods through mathematical formulations and prose, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation details and code are provided in supplementary materials. |
| Open Datasets | No | The environment is custom-defined and described in Figure 2 and Section 6, but no public access information (link, citation) is provided for it as a dataset. |
| Dataset Splits | No | The paper conducts simulations within a defined MDP environment and does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the UCRL learning algorithm but does not specify any software libraries or packages with version numbers used for implementation. |
| Experiment Setup | Yes | The regularity parameter δ in the problems for solving dynamic poisoning attacks is set to 0.0001. In the experiments, we fix R(s0, .) = 2.5 and ϵ = 0.1 |