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..
Model-Free Robust $ฯ$-Divergence Reinforcement Learning Using Both Offline and Online Data
Authors: Kishan Panaganti, Adam Wierman, Eric Mazumdar
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents work that aims to advance the field of Robust Reinforcement Learning for learning robust policies against model parameter mismatches. This work is of a rigorous theoretical nature; hence, the potential societal consequences of our work do not exist, or none of which we feel must be specifically highlighted here. |
| Researcher Affiliation | Academia | Kishan Panaganti 1 Adam Wierman 1 Eric Mazumdar 1 1Computing + Mathematical Sciences Department, California Institute of Technology. Correspondence to: Kishan Panaganti <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Robust ฯ-regularized fitted Q-iteration (RPQ) Algorithm |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available, nor does it provide a link to a code repository for the described methodology. |
| Open Datasets | No | The paper discusses concepts such as "offline dataset DP o" and "adaptive datasets" collected on a "nominal model P o" or by a "data distribution ยต". However, it does not name or provide access information (link, DOI, specific citation with author/year) for any publicly available, identifiable dataset used for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments. Therefore, it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and theoretical analysis. It does not report on computational experiments and therefore does not provide hardware specifications. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and theoretical analysis. It does not report on computational experiments and therefore does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and their theoretical properties. It does not detail an empirical "experimental setup" with specific hyperparameters or system-level training settings for a practical implementation. |