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..
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
Authors: Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban, Adrian Weller, Volkan Cevher
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate the stable performance of our algorithm compared to the standard MCE IRL algorithm under transition dynamics mismatches in both finite and continuous MDP problems. |
| Researcher Affiliation | Collaboration | Luca Viano LIONS, EPFL Yu-Ting Huang EPFL Parameswaran Kamalaruban The Alan Turing Institute Adrian Weller University of Cambridge & The Alan Turing Institute Volkan Cevher LIONS, EPFL |
| Pseudocode | Yes | Algorithm 1 Robust MCE IRL via Markov Game |
| Open Source Code | Yes | Code Repository https://github.com/lviano/Robust_MCE_IRL/tree/master/robust_IRLcode |
| Open Datasets | No | The paper describes generating data within custom GRIDWORLD and OBJECTWORLD environments, rather than using a pre-existing publicly available dataset with a specific link or citation for access. It defines how the environments are set up but does not provide concrete access information for a dataset. |
| Dataset Splits | No | The paper specifies experimental parameters like noise levels (ϵL, ϵE) and algorithm parameter α, but it does not describe specific train/validation/test dataset splits with percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | We used an internal cluster with CPU nodes for the experiments; but we do not have an estimate of the total amount of compute. |
| Software Dependencies | No | The paper mentions using the 'deep MCE IRL algorithm from [44]' but does not provide specific software dependencies with version numbers (e.g., PyTorch version, Python version, specific library versions). |
| Experiment Setup | Yes | We have provided all the training and hyperparameters details in the Experiments section, and in the Appendix. In our experiments, we set T ref to be deterministic, and T to be uniform. Then, one can easily show that ddyn T L,ϵL, T E,ϵE = 2 1 1 |S| |ϵL ϵE|. ...our robust MCE IRL algorithm with different values of α {0.8, 0.85, 0.9, 0.95}... |