Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games
Authors: Qinghua Liu, Csaba Szepesvari, Chi Jin
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,479 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,259 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is purely theoretical and we do not anticipate any potential negative social impacts. Did you run experiments? [N/A] |
| Researcher Affiliation | Collaboration | Qinghua Liu Princeton University EMAIL Csaba Szepesvári Deep Mind and University of Alberta EMAIL Chi Jin Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1 OMLE-Equilibrium, Subroutine 1 Optimistic_Equilibrium(B), Algorithm 2 multi-step OMLE-Equilibrium, Algorithm 3 OMLE-Adversary |
| Open Source Code | No | The paper is purely theoretical and does not provide any statement or link regarding the release of source code. |
| Open Datasets | No | The paper is purely theoretical and does not use or describe any dataset, public or otherwise. |
| Dataset Splits | No | The paper is purely theoretical and does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not mention any hardware specifications for running experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not mention specific software dependencies with version numbers for running experiments. |
| Experiment Setup | No | The paper is purely theoretical and focuses on algorithm design and theoretical guarantees, thus it does not include details on experimental setup such as hyperparameters or system-level training settings. |