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..
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms
Authors: Tengyu Xu, Zhe Wang, Yingbin Liang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is the first theoretical study establishing that AC and NAC attain orderwise performance improvement over PG and NPG under infinite horizon due to the incorporation of critic. |
| Researcher Affiliation | Academia | Department of ECE, The Ohio State University |
| Pseudocode | Yes | Algorithm 1 Actor-critic (AC) and natural actor-critic (NAC) online algorithms; Algorithm 2 Minibatch-TD(sini, π, φ, β, Tc, M) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical experiments on a specific dataset; thus, it does not describe any training dataset or its availability. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments; thus, it does not specify any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is a theoretical study and does not report on empirical experiments; therefore, it does not specify any hardware used. |
| Software Dependencies | No | The paper is a theoretical study and does not report on empirical experiments; therefore, it does not specify any software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper describes algorithms with general parameters like 'actor stepsize α, critic stepsize β, regularization λ' but does not provide specific hyperparameter values or system-level training settings for an empirical experimental setup. |