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..
A Theoretical Justification for Asymmetric Actor-Critic Algorithms
Authors: Gaspard Lambrechts, Damien Ernst, Aditya Mahajan
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose such a justification for asymmetric actor-critic algorithms with linear function approximators by adapting a finite-time convergence analysis to this setting. The resulting finite-time bound reveals that the asymmetric critic eliminates error terms arising from aliasing in the agent state. |
| Researcher Affiliation | Academia | 1Montefiore Institute, University of Li ege 2Department of Electrical and Computer Engineering, Mc Gill University. Correspondence to: Gaspard Lambrechts <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 m-step temporal difference learning algorithm |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving specific datasets. It references the 'Tiger POMDP' as an example (Figure 1) but not as a dataset used for empirical evaluation with public access information. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus it does not specify any dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not describe any empirical experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not include empirical experiments, so no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper presents a theoretical justification for algorithms and does not include any experimental results or specific details about an experimental setup, such as hyperparameters or training configurations. |