Reward-Free Kernel-Based Reinforcement Learning
Authors: Sattar Vakili, Farhang Nabiei, Da-Shan Shiu, Alberto Bernacchia
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider a broad range of kernel-based function approximations, including non-smooth kernels, and propose an algorithm based on adaptive domain partitioning. We show that our algorithm achieves order-optimal sample complexity for a large class of common kernels, which includes Mat ern and Neural Tangent kernels. |
| Researcher Affiliation | Industry | 1Media Tek Research. Correspondence to: Sattar Vakili <sattar.vakili@mtkresearch.com>. |
| Pseudocode | Yes | Algorithm 1 Exploration Phase, Algorithm 2 Planning Phase |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on sample complexity analysis for a reward-free RL framework, rather than conducting empirical studies on a specific dataset. Therefore, it does not mention dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on specific datasets, hence no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and sample complexity analysis. It does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and describes algorithms and mathematical analysis. It does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and sample complexity analysis. It does not describe any specific experimental setup, hyperparameters, or training configurations. |