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..
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Authors: Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We do not include any experiments. |
| Researcher Affiliation | Academia | Paria Rashidinejad Department of EECS UC Berkeley Berkeley, CA, 94709 EMAIL; Banghua Zhu Department of EECS UC Berkeley Berkeley, CA, 94709 EMAIL; Cong Ma Department of Statistics University of Chicago Chicago, IL, 60637 EMAIL; Jiantao Jiao Department of EECS UC Berkeley Berkeley, CA, 94709 EMAIL; Stuart Russell Department of EECS UC Berkeley Berkeley, CA, 94709 EMAIL |
| Pseudocode | Yes | Algorithm 1 LCB for bandits and contextual bandits; Algorithm 2 Ofο¬ine value iteration with LCB (VI-LCB) |
| Open Source Code | No | We do not include any experiments. Our work does not use any assets. |
| Open Datasets | No | The paper explicitly states: 'We do not include any experiments.' and 'Our work does not use any assets.', indicating no dataset was used or provided by the authors for their work. |
| Dataset Splits | No | We do not include any experiments. |
| Hardware Specification | No | We do not include any experiments. |
| Software Dependencies | No | We do not include any experiments. |
| Experiment Setup | No | We do not include any experiments. |