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..
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
Authors: Angeliki Kamoutsi, Goran Banjac, John Lygeros
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further present an equivalent no-regret online-learning interpretation.In Appendix E we provide preliminary empirical results on a simple tabular MDP in order to illustrate our formulations and theoretical results. |
| Researcher Affiliation | Academia | 1Automatic Control Laboratory, ETH Zurich, Switzerland. Correspondence to: Angeliki Kamoutsi <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Stochastic Primal-Dual Lf D |
| Open Source Code | No | The paper does not contain an explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions using 'a finite set of expert demonstrations' and conducting 'preliminary empirical results on a simple tabular MDP' but does not provide specific access information (link, DOI, formal citation) for any publicly available dataset. |
| Dataset Splits | No | The paper does not specify exact percentages or sample counts for training, validation, or test splits, nor does it reference predefined splits with citations. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, libraries, or solver names with their version numbers needed for replication. |
| Experiment Setup | Yes | Input: number of iterations N, step-size η, radius β and Set θ1,i = 1 nµ , i [nµ], w1,i = 1 nc , i [nc], λ = 0 and learning rate η = 1 γ β Nnµ . |