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..
Planning and Learning with Stochastic Action Sets
Authors: Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we offer a simple empirical demonstration of the importance of accounting for stochastic action availability when computing an MDP policy. Additional discussion and full proofs of all results can be found in a longer version of this paper [Boutilier et al., 2018]. |
| Researcher Affiliation | Industry | Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov and Dale Schuurmans Google Research EMAIL |
| Pseudocode | No | No clearly labeled pseudocode or algorithm blocks were found. Algorithms are described in paragraph form. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. It mentions open-source in the context of existing tools but not for their own implementation. |
| Open Datasets | No | The paper uses "a real-world road network (Fig. 1) in the San Francisco Bay Area" for its empirical illustration but does not provide access information (link, DOI, citation) for this specific dataset. |
| Dataset Splits | No | The empirical illustration describes a routing problem without specifying dataset splits (e.g., training, validation, test percentages or sample counts). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | Yes | The optimal policies for different choices p = 0.1, 0.2 and 0.4 are depicted in Fig. 1, where line thickness and color indicate traversal probabilities under the corresponding optimal policies. We see that lower values of p lead to policies with more redundancy (i.e., more alternate routes). |