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..
Optimal Regret Bounds via Low-Rank Structured Variation in Non-Stationary Reinforcement Learning
Authors: Tuan Dam
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Justification: We provide a theoretical study with an improved regret bound for nonstationary reinforcement learning. The results of the paper are fully theoretical. |
| Researcher Affiliation | Academia | Tuan Dam Hanoi University of Science and Technology, Hanoi, Vietnam EMAIL |
| Pseudocode | Yes | Algorithm 1 Randomised SVD with power iterations Algorithm 2 Incremental truncated SVD warm-start (column append) Algorithm 3 SVUCRL Algorithm 4 Extended Value Iteration |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: We provide a theoretical study with an improved regret bound for nonstationary reinforcement learning. |
| Open Datasets | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: We provide a theoretical study with an improved regret bound for nonstationary reinforcement learning. |
| Dataset Splits | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: This is a theoretical paper. |
| Hardware Specification | No | Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] Justification: This is a theoretical paper. |
| Software Dependencies | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: This is a theoretical paper. |
| Experiment Setup | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: This is a theoretical paper. |