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 [1].
Accelerating Value Iteration with Anchoring
Authors: Jongmin Lee, Ernest Ryu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work focuses on the theoretical aspects of reinforcement learning. There are no negative social impacts that we anticipate from our theoretical results. |
| Researcher Affiliation | Academia | Jongmin Lee1 Ernest K. Ryu1,2 1Department of Mathematical Science, Seoul National University 2Interdisciplinary Program in Artificial Intelligence, Seoul National University |
| Pseudocode | No | The paper describes algorithms (Anc-VI, Apx-Anc-VI, GS-Anc-VI) through mathematical equations and definitions but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper is theoretical and does not mention providing any open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe any experimental setup involving datasets or their public availability. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for validation purposes. There are no experiments conducted with data. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies for experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |