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..
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
Authors: Nathan Kallus, Angela Zhou
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop approximations based on nonconvex projected gradient descent and demonstrate the resulting bounds empirically. We then demonstrate the approach on a gridworld task with unobserved confounding. In Figure 5, we study the finite-sample properties of the bounds estimator, plotting ^RT e ^R10k e for differing trajectory lengths on a logarithmic grid, T 2 [250, 10000], and standard errors averaged over 50 replications. |
| Researcher Affiliation | Academia | Nathan Kallus School of Operations Research and Information Engineering Cornell University and Cornell Tech EMAIL Angela Zhou School of Operations Research and Information Engineering Cornell University and Cornell Tech EMAIL |
| Pseudocode | Yes | Algorithm 1 Nonconvex nonconvex-projected gradient descent |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes custom-built simulation environments ('Confounded random walk', '3x3 confounded windy gridworld') but does not refer to or provide access to any publicly available datasets. |
| Dataset Splits | No | The paper describes simulation parameters like 'trajectory lengths' and 'replications' but does not specify conventional training, validation, or test dataset splits for a pre-existing dataset. |
| Hardware Specification | No | The paper mentions using 'Gurobi version 9' but does not provide any specific hardware details such as CPU, GPU models, or memory specifications used for the experiments. |
| Software Dependencies | Yes | We compute bounds via global optimization with Gurobi version 9 |
| Experiment Setup | Yes | In Fig. 3, we vary the underlying transition model, varying pu1 = pu2 on a grid [0.1, 0.45], and we plot the varying bounds with action-marginal control variates. The true underlying behavior policy takes action a = 1 with probability (1 | s1, u1) = (1 | s2, u1) = 1/4 (and the complementary probability when u = u2). In Figure 5, we study the ο¬nite-sample properties of the bounds estimator, plotting ^RT e ^R10k e for differing trajectory lengths on a logarithmic grid, T 2 [250, 10000], and standard errors averaged over 50 replications. |