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..
Cooperative Online Learning in Stochastic and Adversarial MDPs
Authors: Tal Lancewicki, Aviv Rosenberg, Yishay Mansour
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We thoroughly analyze all relevant settings, highlight the challenges and differences between the models, and prove nearly-matching regret lower and upper bounds. To our knowledge, we are the first to consider cooperative reinforcement learning (RL) with either non-fresh randomness or in adversarial MDPs. |
| Researcher Affiliation | Collaboration | 1Tel Aviv University 2Google Research, Tel Aviv. |
| Pseudocode | Yes | Algorithm 1 COOP-ULCAE |
| Open Source Code | No | No statements or links indicating the release of open-source code for the methodology described in this paper. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not mention using specific datasets or providing access information for them. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not mention dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not mention specific hardware used for experiments. |
| Software Dependencies | No | The paper focuses on theoretical analysis and algorithm design; it does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe a concrete experimental setup with hyperparameters or training settings. |