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..
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
Authors: Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | All proofs and experiments are included in Appendix. In this section, we conduct proof-of-concept numerical experiments to verify our theoretical results for both policy-based and value-based algorithms. |
| Researcher Affiliation | Collaboration | 1 Provable Responsible AI and Data Analytics Lab 2King Abdullah University of Science and Technology, Saudi Arabia 3Wayne State University, USA 4Microsoft Research, India. |
| Pseudocode | Yes | Our general framework, Private-Heavy-UCBVI algorithm, is presented in Algorithm 1. See Algorithm 3 for details. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We consider the standard tabular MDP environment River Swim (Osband et al., 2013) |
| Dataset Splits | No | The paper does not explicitly describe training/test/validation dataset splits, as it uses a simulated MDP environment where data is generated interactively over episodes. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | We set all the parameters in our proposed algorithms in the same order as the theoretical results. We tune the learning rate η and the scaling of the confidence interval to obtain the best results. We run 10 independent experiments, each consisting of K = 2 × 10^4 episodes. Each episode is reset every H = 20 step. |