Batch Value-function Approximation with Only Realizability
Authors: Tengyang Xie, Nan Jiang
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our algorithm, BVFT, breaks the hardness conjecture (albeit under a stronger notion of exploratory data) via a tournament procedure that reduces the learning problem to pairwise comparison, and solves the latter with the help of a state-action-space partition constructed from the compared functions. We present the algorithm in Section 4 and prove its sample complexity in Sections 5 and 6. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Illinois at Urbana-Champaign, Illinois, USA. Correspondence to: Nan Jiang <nanjiang@illinois.edu>. |
| Pseudocode | Yes | Algorithm 1 Batch Value-Function Tournament (BVFT) |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper assumes access to a "batch dataset D" for theoretical analysis but does not specify a real-world, publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus it does not provide information about dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not present empirical experiments, thus it does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not present empirical experiments, thus it does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not present empirical experiments, thus it does not provide details about experimental setup, such as hyperparameter values or training configurations. |