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..
Off-Policy Evaluation in Partially Observable Environments
Authors: Guy Tennenholtz, Uri Shalit, Shie Mannor10276-10283
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the pitfalls of off-policy evaluation in POMDPs using a well-known off-policy method, Importance Sampling, and compare it with our result on synthetic medical data. In Section 6, we experiment with the results of Theorem 2 and the IS variant constructed in this section on a finite-sample dataset generated by a synthetic medical environment. In this work we experimented with a tabular environment. |
| Researcher Affiliation | Academia | Guy Tennenholtz Technion Institute of Technology Shie Mannor Technion Institute of Technology Uri Shalit Technion Institute of Technology |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper states: 'In our experiments we construct a synthetic medical environment...' and 'Ten million trajectories were sampled from the policy πb over a horizon of 4 time steps for each environment.' No concrete access information (link, DOI, repository name, formal citation) is provided for this synthetic dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We denote σ(x) = 1 1+e x . The environment consists of a patient s (observed) medical state z. ... The observed state space Z, unobserved state space U, and observation space O were composed of two binary features each. We run the experiment in three environments, corresponding to different settings of the vectors c meant to illustrate different behaviors of our methods. Ten million trajectories were sampled from the policy πb over a horizon of 4 time steps for each environment. Figure 3 depicts the cumulative reward of πe, πb, and their corresponding estimates according to Theorem 2 and the IS weights wk i = π(i) e (ai|ho i ) P b(ai|ho i ) , for different values of α. |