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..
Operator Splitting Value Iteration
Authors: Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate both OS-VI and OS-Dyna in a finite MDP and compare them with existing methods. Here we present the results for the Control problem on a modified cliffwalk environment in a 6x6 grid with 4 actions (UP, DOWN, LEFT, RIGHT). The left plot in Figure 2 shows the convergence of OS-VI compared to VI and the solutions the model itself would lead to. The plot shows normalized error of Vk V * w.r.t V * . (Right) Comparison of OS-Dyna with Dyna and Q-Learning in the RL setting. |
| Researcher Affiliation | Collaboration | Amin Rakhsha1,2 Andrew Wang1,2 Mohammad Ghavamzadeh3 Amir-massoud Farahmand2,1 1Department of Computer Science, University of Toronto 2Vector Institute 3Google Research |
| Pseudocode | Yes | Algorithm 1 OS-Dyna |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] The details are in the supplementary material. |
| Open Datasets | No | The paper mentions a 'modified cliffwalk environment in a 6x6 grid' but does not provide a link or citation to a public dataset, nor does it explicitly state its public availability. |
| Dataset Splits | No | The paper describes an RL setup where 'algorithms are given a sample (Xt, At, Rt, X't)' but does not specify traditional training, validation, or test dataset splits. |
| Hardware Specification | No | The experiments are simple and can be run on a personal computer. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers. |
| Experiment Setup | Yes | The learning rates are constant α for iterations t ≥ N and then decay in the form of αt = α/(t − N) afterwards. We have fine-tuned the learning rate schedule for each algorithm separately for the best results. |