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..
First-Order Methods for Wasserstein Distributionally Robust MDP
Authors: Julien Grand Clement, Christian Kroer
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that our algorithm is significantly more scalable than state-of-the-art approaches across several domains. |
| Researcher Affiliation | Academia | 1IEOR Department, Columbia University. |
| Pseudocode | Yes | Algorithm 1 First-order Method for Wasserstein DR-MDP |
| Open Source Code | No | The paper does not provide any specific links to source code or state that the code is publicly available. |
| Open Datasets | No | For each MDP instance, we generate the sampled kernels ˆy1, ..., ˆy N by considering N small random (Garnet) perturbations around the true nominal kernel y0 (see Appendix F). |
| Dataset Splits | No | The paper describes generating MDP instances and evaluating performance based on running times and optimality criteria, but it does not specify train/validation/test dataset splits for a fixed dataset. |
| Hardware Specification | Yes | We run our simulations on a laptop with 2.2 GHz Intel Core i7 and 8 GB of RAM. |
| Software Dependencies | Yes | We implement our algorithms in Python 3.7.3, using Gurobi 8.1.1 to solve any linear/quadratic optimization program involved. |
| Experiment Setup | Yes | All figures in this section show the running times of the algorithms before returning an ϵ-optimal policy with ϵ = 0.1. We initialize all algorithms with v0 = 0. The running times are averaged across 5 instances by changing the seeds for sampling the N kernels around y0. |