Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces
Authors: Haifang Li, Yingce Xia, Wensheng Zhang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |