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..
Dynamic Regret of Online Markov Decision Processes
Authors: Peng Zhao, Long-Fei Li, Zhi-Hua Zhou
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For the three models, we propose novel online ensemble algorithms and establish their dynamic regret guarantees respectively, in which the results for episodic (loop-free) SSP are provably minimax optimal in terms of time horizon and certain non-stationarity measure. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University. Correspondence to: Zhi-Hua Zhou <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 DO-REPS Algorithm 2 CODO-REPS Algorithm 3 REDO-REPS |
| Open Source Code | No | The paper does not mention providing open-source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper focuses on theoretical contributions and algorithm design. It does not conduct experiments on datasets, thus no information about publicly available or open datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with datasets. Therefore, no information regarding training, validation, or test dataset splits is provided. |
| Hardware Specification | No | The paper focuses on theoretical algorithms and their guarantees. It does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs. It does not mention any specific software dependencies with version numbers required for implementation or execution. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |