Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Authors: Chris Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents a theoretical analysis of such policies and provides the first regret and sample-complexity bounds for reinforcement learning with myopic exploration. |
| Researcher Affiliation | Collaboration | 1Google Research 2Tel Aviv University 3Courant Institute of Mathematical Sciences 4Cornell University. |
| Pseudocode | Yes | Algorithm 1: RL with myopic exploration |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is open or available. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not mention any specific hardware used for experiments, consistent with a theoretical work. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a concrete experimental setup with hyperparameters or system-level training settings. |