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..
Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces
Authors: Haifang Li, Yingce Xia, Wensheng Zhang
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We carry out theoretical analysis of LSTD(λ)-RP, and provide meaningful upper bounds of the estimation error, approximation error and total generalization error. These results demonstrate that LSTD(λ)-RP can benefit from random projection and eligibility traces strategies, and LSTD(λ)-RP can achieve better performances than prior LSTDRP and LSTD(λ) algorithms. |
| Researcher Affiliation | Academia | Haifang Li1, Yingce Xia2 and Wensheng Zhang1 1 Institute of Automation, Chinese Academy of Sciences, Beijing, China 2 University of Science and Technology of China, Hefei, Anhui, China |
| Pseudocode | Yes | Algorithm 1: LSTD(λ)-RP Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use a specific public dataset for empirical evaluation. It refers to a 'sampled trajectory {Xt}n t=1' within its theoretical framework, but this is not a concrete, publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset usage, therefore no validation splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for its analysis or algorithms. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with specific hyperparameters or training configurations. |