Information-Theoretic Considerations in Batch Reinforcement Learning
Authors: Jinglin Chen, Nan Jiang
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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. |