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..
Policy Evaluation Using the Ω-Return
Authors: Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical studies that suggest that it is superior to the λ-return and γ-return for a variety of problems. We propose a method for approximating the Ω-return, and show that it outperforms the λ and γ-returns on a range of off-policy evaluation problems. Figures 1(g), 2(g), 3(g), and 4(g) show the mean squared error (MSE) of value estimates when using various methods. |
| Researcher Affiliation | Collaboration | Philip S. Thomas University of Massachusetts Amherst Carnegie Mellon University Scott Niekum University of Texas at Austin Georgios Theocharous Adobe Research George Konidaris Duke University |
| Pseudocode | Yes | Pseudocode for approximating the Ω-return is provided in Algorithm 1. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper mentions domain names like '5 × 5 gridworld', 'mountain car domain', 'digital marketing problem', and 'DAS1' but does not provide concrete access information (specific links, DOIs, repository names, or formal citations with authors/year) for these datasets. |
| Dataset Splits | No | The paper mentions varying numbers of trajectories used for estimating the covariance matrix (e.g., '5 trajectories', '10,000 trajectories') but does not specify explicit train/validation/test dataset splits, percentages, or cross-validation setup for reproducing the experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU/GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers) used for the experiments. |
| Experiment Setup | Yes | We select the k1 and k2 that minimize the mean squared error between ˆΩ(i, i) and vi, and set v+ and v L directly from the data. |