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..
Information-Theoretic Considerations in Batch Reinforcement Learning
Authors: Jinglin Chen, Nan Jiang
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we revisit these assumptions and provide theoretical results towards answering the above questions, and make steps towards a deeper understanding of value-function approximation. |
| Researcher Affiliation | Academia | 1University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. |
| Pseudocode | No | The paper describes algorithms such as Fitted Q-Iteration (FQI) conceptually ( |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and describes general batch datasets ( |
| Dataset Splits | No | The paper is theoretical and does not perform experiments with specific dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not discuss hardware specifications for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe a specific experimental setup, hyperparameters, or training configurations. |