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..
Interactive Teaching Algorithms for Inverse Reinforcement Learning
Authors: Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher, Adish Singla
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments with a car driving simulator environment show that the learning progress can be speeded up drastically as compared to an uninformative teacher. (...) 6 Experimental Evaluation |
| Researcher Affiliation | Academia | 1LIONS, EPFL 2Max Planck Institute for Software Systems (MPI-SWS) |
| Pseudocode | Yes | Algorithm 1 Interactive Teaching Framework (...) Algorithm 2 Sequential MCE-IRL (...) Algorithm 3 OMNITEACHER for sequential MCE-IRL (...) Algorithm 4 BBOXTEACHER for a sequential IRL learner |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes the creation of a 'car driving simulator environment' and defines 'tasks' within it, which are used to generate demonstrations for training. However, it does not provide concrete access information (link, DOI, formal citation for a public dataset) to the generated demonstrations or the simulator environment itself as a publicly available dataset. |
| Dataset Splits | No | The paper does not explicitly describe a validation set or a standard train/validation/test split for its experimental data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | For BBOXTEACHER in Algorithm 4, we use B = 5 and k = 5. (...) We use n = 5 lanes of each task (i.e., 40 lanes in total). (...) We use similar experimental settings as in Section 6.1 (i.e., n = 5, averaging 10 runs, etc.). |