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..
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Authors: Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To address this inadequacy, we study a pessimistic variant of Q-learning in the context of finitehorizon Markov decision processes, and characterize its sample complexity under the single-policy concentrability assumption which does not require the full coverage of the state-action space. In addition, a variance-reduced pessimistic Qlearning algorithm is proposed to achieve nearoptimal sample complexity. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA 2Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA. |
| Pseudocode | Yes | Algorithm 1 LCB-Q for offline RL, Algorithm 2 Offline LCB-Q-Advantage RL |
| Open Source Code | No | The paper does not contain any statement or link indicating the release of source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or specify any publicly available dataset for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, including hyperparameters or system-level training settings. |