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..
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Authors: Nan Jiang, Lihong Li
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the estimator s accuracy in several benchmark problems, and illustrate its use as a subroutine in safe policy improvement. 6. Experiments |
| Researcher Affiliation | Collaboration | Nan Jiang EMAIL Computer Science & Engineering, University of Michigan Lihong Li EMAIL Microsoft Research |
| Pseudocode | No | The paper defines estimators using mathematical equations (e.g., Eqn. 10) but does not provide any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not include an explicit statement about releasing its source code or provide any links to a code repository for the methodology described. |
| Open Datasets | Yes | In the last domain, we use the donation dataset from KDD Cup 1998 (Hettich & Bay, 1999) |
| Dataset Splits | Yes | We therefore split Deval further into two subsets Dreg and Dtest, estimate b Q from Dreg and apply DR on Dtest. we partition Deval into k subsets, apply Eqn.(8) to each subset with b Q estimated from the remaining data, and finally average the estimate over all subsets. we split |D| so that |Dtrain|/|D| {0.2, 0.4, 0.6, 0.8} |
| Hardware Specification | No | The paper does not provide specific hardware details (like CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | Model fitting We use state aggregations: the two state variables are multiplied by 26 and 28 respectively, and the rounded integers are treated as the abstract state. We then estimate an MDP model from data using a tabular approach. mix ฯtrain and ฯ0 with rate ฮฑ {0, 0.1, . . . , 0.9} |