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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Admissible Policy Teaching through Reward Design
Authors: Kiarash Banihashem, Adish Singla, Jiarui Gan, Goran Radanovic6037-6045
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,066 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,633 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design a local search algorithm to solve the surrogate problem and showcase its utility using simulation-based experiments. |
| Researcher Affiliation | Academia | Max Planck Institute for Software Systems EMAIL |
| Pseudocode | Yes | Algorithm 1. CONSTRAIN&OPTIMIZE |
| Open Source Code | No | For details regarding the experiments and code, please refer to the full version of our paper (Banihashem et al. 2022). |
| Open Datasets | No | As an experimental testbed, we consider three simple navigation environments, shown in Figure 2. |
| Dataset Splits | No | The paper describes custom environments and mentions parameters, but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not specify software names with version numbers. |
| Experiment Setup | Yes | By default, we set the parameters γ = 0.9, λ = 1.0 and ϵ = 0.1. |