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.
Differentially Private Reinforcement Learning with Self-Play
Authors: Dan Qiao, Yu-Xiang Wang
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 33,577 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,058 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 | Then we design a provably efficient algorithm based on optimistic Nash value iteration and privatization of Bernstein-type bonuses. The algorithm is able to satisfy JDP and LDP requirements when instantiated with appropriate privacy mechanisms. Furthermore, for both notions of DP, our regret bound generalizes the best known result under the single-agent RL case, while our regret could also reduce to the best known result for multi-agent RL without privacy constraints. |
| Researcher Affiliation | Academia | Dan Qiao Department of Computer Science & Engineering University of California, San Diego San Diego, CA 92093 EMAIL Yu-Xiang Wang Halฤฑcฤฑo glu Data Science Institute University of California, San Diego San Diego, CA 92093 EMAIL |
| Pseudocode | Yes | Algorithm 1 Differentially Private Optimistic Nash Value Iteration (DP-Nash-VI) |
| Open Source Code | No | This is a theory paper and we do not conduct experiments. The paper does not provide any statement or link regarding the release of source code. |
| Open Datasets | No | This is a theory paper and we do not conduct experiments. As such, no datasets are used for training or mentioned as publicly available. |
| Dataset Splits | No | This is a theory paper and we do not conduct experiments. Therefore, no dataset splits for validation are provided. |
| Hardware Specification | No | This is a theory paper and we do not conduct experiments. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | This is a theory paper and we do not conduct experiments. Therefore, no specific software dependencies or versions for experimental reproduction are listed. |
| Experiment Setup | No | This is a theory paper and we do not conduct experiments. Therefore, no experimental setup details such as hyperparameters or system-level training settings are provided. |